ultralytics 8.3.71 require explicit torch.nn usage (#19067)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: RizwanMunawar <chr043416@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
parent
17450e9646
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5bca9341e8
10 changed files with 50 additions and 51 deletions
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@ -9,7 +9,6 @@ from pathlib import Path
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import thop
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import torch
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import torch.nn as nn
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from ultralytics.nn.modules import (
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AIFI,
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@ -88,7 +87,7 @@ from ultralytics.utils.torch_utils import (
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)
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class BaseModel(nn.Module):
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class BaseModel(torch.nn.Module):
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"""The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family."""
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def forward(self, x, *args, **kwargs):
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@ -151,7 +150,7 @@ class BaseModel(nn.Module):
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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if embed and m.i in embed:
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embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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if m.i == max(embed):
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return torch.unbind(torch.cat(embeddings, 1), dim=0)
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return x
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@ -170,12 +169,9 @@ class BaseModel(nn.Module):
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the provided list.
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Args:
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m (nn.Module): The layer to be profiled.
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m (torch.nn.Module): The layer to be profiled.
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x (torch.Tensor): The input data to the layer.
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dt (list): A list to store the computation time of the layer.
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Returns:
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None
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"""
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c = m == self.model[-1] and isinstance(x, list) # is final layer list, copy input as inplace fix
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flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0 # GFLOPs
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@ -195,7 +191,7 @@ class BaseModel(nn.Module):
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computation efficiency.
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Returns:
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(nn.Module): The fused model is returned.
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(torch.nn.Module): The fused model is returned.
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"""
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if not self.is_fused():
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for m in self.model.modules():
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@ -229,7 +225,7 @@ class BaseModel(nn.Module):
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Returns:
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(bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
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"""
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bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
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bn = tuple(v for k, v in torch.nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
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return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model
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def info(self, detailed=False, verbose=True, imgsz=640):
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@ -304,7 +300,7 @@ class DetectionModel(BaseModel):
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self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg) # cfg dict
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if self.yaml["backbone"][0][2] == "Silence":
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LOGGER.warning(
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"WARNING ⚠️ YOLOv9 `Silence` module is deprecated in favor of nn.Identity. "
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"WARNING ⚠️ YOLOv9 `Silence` module is deprecated in favor of torch.nn.Identity. "
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"Please delete local *.pt file and re-download the latest model checkpoint."
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)
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self.yaml["backbone"][0][2] = "nn.Identity"
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@ -458,20 +454,22 @@ class ClassificationModel(BaseModel):
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name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module
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if isinstance(m, Classify): # YOLO Classify() head
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if m.linear.out_features != nc:
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m.linear = nn.Linear(m.linear.in_features, nc)
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elif isinstance(m, nn.Linear): # ResNet, EfficientNet
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m.linear = torch.nn.Linear(m.linear.in_features, nc)
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elif isinstance(m, torch.nn.Linear): # ResNet, EfficientNet
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if m.out_features != nc:
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setattr(model, name, nn.Linear(m.in_features, nc))
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elif isinstance(m, nn.Sequential):
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setattr(model, name, torch.nn.Linear(m.in_features, nc))
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elif isinstance(m, torch.nn.Sequential):
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types = [type(x) for x in m]
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if nn.Linear in types:
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i = len(types) - 1 - types[::-1].index(nn.Linear) # last nn.Linear index
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if torch.nn.Linear in types:
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i = len(types) - 1 - types[::-1].index(torch.nn.Linear) # last torch.nn.Linear index
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if m[i].out_features != nc:
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m[i] = nn.Linear(m[i].in_features, nc)
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elif nn.Conv2d in types:
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i = len(types) - 1 - types[::-1].index(nn.Conv2d) # last nn.Conv2d index
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m[i] = torch.nn.Linear(m[i].in_features, nc)
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elif torch.nn.Conv2d in types:
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i = len(types) - 1 - types[::-1].index(torch.nn.Conv2d) # last torch.nn.Conv2d index
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if m[i].out_channels != nc:
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m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
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m[i] = torch.nn.Conv2d(
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m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None
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)
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def init_criterion(self):
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"""Initialize the loss criterion for the ClassificationModel."""
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@ -587,7 +585,7 @@ class RTDETRDetectionModel(DetectionModel):
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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if embed and m.i in embed:
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embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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if m.i == max(embed):
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return torch.unbind(torch.cat(embeddings, 1), dim=0)
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head = self.model[-1]
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@ -663,7 +661,7 @@ class WorldModel(DetectionModel):
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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if embed and m.i in embed:
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embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
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if m.i == max(embed):
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return torch.unbind(torch.cat(embeddings, 1), dim=0)
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return x
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@ -684,7 +682,7 @@ class WorldModel(DetectionModel):
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return self.criterion(preds, batch)
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class Ensemble(nn.ModuleList):
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class Ensemble(torch.nn.ModuleList):
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"""Ensemble of models."""
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def __init__(self):
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@ -887,7 +885,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
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for m in ensemble.modules():
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if hasattr(m, "inplace"):
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m.inplace = inplace
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elif isinstance(m, nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
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elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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# Return model
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@ -922,7 +920,7 @@ def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
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for m in model.modules():
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if hasattr(m, "inplace"):
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m.inplace = inplace
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elif isinstance(m, nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
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elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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# Return model and ckpt
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@ -946,7 +944,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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depth, width, max_channels = scales[scale]
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if act:
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Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
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Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = torch.nn.SiLU()
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if verbose:
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LOGGER.info(f"{colorstr('activation:')} {act}") # print
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@ -982,7 +980,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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C3,
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C3TR,
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C3Ghost,
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nn.ConvTranspose2d,
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torch.nn.ConvTranspose2d,
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DWConvTranspose2d,
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C3x,
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RepC3,
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@ -1048,7 +1046,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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n = 1
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elif m is ResNetLayer:
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c2 = args[1] if args[3] else args[1] * 4
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elif m is nn.BatchNorm2d:
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elif m is torch.nn.BatchNorm2d:
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args = [ch[f]]
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elif m is Concat:
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c2 = sum(ch[x] for x in f)
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@ -1073,7 +1071,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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else:
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c2 = ch[f]
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m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
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m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
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t = str(m)[8:-2].replace("__main__.", "") # module type
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m_.np = sum(x.numel() for x in m_.parameters()) # number params
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m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
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@ -1084,7 +1082,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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if i == 0:
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ch = []
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ch.append(c2)
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return nn.Sequential(*layers), sorted(save)
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return torch.nn.Sequential(*layers), sorted(save)
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def yaml_model_load(path):
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@ -1126,7 +1124,7 @@ def guess_model_task(model):
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Guess the task of a PyTorch model from its architecture or configuration.
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Args:
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model (nn.Module | dict): PyTorch model or model configuration in YAML format.
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model (torch.nn.Module | dict): PyTorch model or model configuration in YAML format.
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Returns:
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(str): Task of the model ('detect', 'segment', 'classify', 'pose').
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@ -1154,7 +1152,7 @@ def guess_model_task(model):
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with contextlib.suppress(Exception):
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return cfg2task(model)
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# Guess from PyTorch model
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if isinstance(model, nn.Module): # PyTorch model
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if isinstance(model, torch.nn.Module): # PyTorch model
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for x in "model.args", "model.model.args", "model.model.model.args":
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with contextlib.suppress(Exception):
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return eval(x)["task"]
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