From b6cdb5d4f82b8915ba9bf0ca77a7a526d1dd59e5 Mon Sep 17 00:00:00 2001 From: zhiqwang Date: Wed, 3 Mar 2021 10:55:30 -0500 Subject: [PATCH] Fix docs and copyright statements --- yolort/models/backbone_utils.py | 7 +++---- yolort/models/yolo.py | 7 +++---- yolort/models/yolotr.py | 11 ++++++----- 3 files changed, 12 insertions(+), 13 deletions(-) diff --git a/yolort/models/backbone_utils.py b/yolort/models/backbone_utils.py index 4b1b87ea..0f29dd0c 100644 --- a/yolort/models/backbone_utils.py +++ b/yolort/models/backbone_utils.py @@ -4,7 +4,6 @@ from . import darknet from .path_aggregation_network import PathAggregationNetwork -from .common import BottleneckCSP, C3 from typing import List, Optional @@ -53,7 +52,7 @@ def darknet_pan_backbone( version: str = 'v4.0', ): """ - Constructs a specified ResNet backbone with PAN on top. Freezes the specified number of + Constructs a specified DarkNet backbone with PAN on top. Freezes the specified number of layers in the backbone. Examples:: @@ -71,12 +70,12 @@ def darknet_pan_backbone( >>> ('2', torch.Size([1, 512, 2, 2]))] Args: - backbone_name (string): resnet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1', + backbone_name (string): darknet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1', 'darknet_m_r3_1', 'darknet_l_r3_1', 'darknet_s_r4_0', 'darknet_m_r4_0', 'darknet_l_r4_0' norm_layer (torchvision.ops): it is recommended to use the default value. For details visit: (https://github.com/facebookresearch/maskrcnn-benchmark/issues/267) pretrained (bool): If True, returns a model with backbone pre-trained on Imagenet - trainable_layers (int): number of trainable (not frozen) resnet layers starting from final block. + trainable_layers (int): number of trainable (not frozen) darknet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. version (str): ultralytics release version: v3.1 or v4.0 """ diff --git a/yolort/models/yolo.py b/yolort/models/yolo.py index a4ed8233..0af3cce3 100644 --- a/yolort/models/yolo.py +++ b/yolort/models/yolo.py @@ -1,5 +1,4 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# Modified by Zhiqiang Wang (me@zhiqwang.com) +# Copyright (c) 2020, Zhiqiang Wang. All Rights Reserved. import warnings import torch @@ -306,8 +305,8 @@ def yolov5_darknet_pan_l_r40(pretrained: bool = False, progress: bool = True, nu def yolov5_darknet_pan_s_tr(pretrained: bool = False, progress: bool = True, num_classes: int = 80, **kwargs: Any) -> YOLO: - r"""yolov5 small release 4.0 model from - `"ultralytics/yolov5" `_. + r"""yolov5 small with a transformer block model from + `"dingyiwei/yolov5" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr diff --git a/yolort/models/yolotr.py b/yolort/models/yolotr.py index 24388c47..070929b9 100644 --- a/yolort/models/yolotr.py +++ b/yolort/models/yolotr.py @@ -1,3 +1,4 @@ +# Copyright (c) 2021, Zhiqiang Wang. All Rights Reserved. from torch import nn from .common import Conv, C3 @@ -18,13 +19,13 @@ def darknet_pan_tr_backbone( version: str = 'v4.0', ): """ - Constructs a specified ResNet backbone with PAN on top. Freezes the specified number of + Constructs a specified DarkNet backbone with PAN on top. Freezes the specified number of layers in the backbone. Examples:: - >>> from models.backbone_utils import darknet_pan_backbone - >>> backbone = darknet_pan_backbone('darknet3_1', pretrained=True, trainable_layers=3) + >>> from models.backbone_utils import darknet_pan_tr_backbone + >>> backbone = darknet_pan_tr_backbone('darknet3_1', pretrained=True, trainable_layers=3) >>> # get some dummy image >>> x = torch.rand(1, 3, 64, 64) >>> # compute the output @@ -36,12 +37,12 @@ def darknet_pan_tr_backbone( >>> ('2', torch.Size([1, 512, 2, 2]))] Args: - backbone_name (string): resnet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1', + backbone_name (string): darknet architecture. Possible values are 'DarkNet', 'darknet_s_r3_1', 'darknet_m_r3_1', 'darknet_l_r3_1', 'darknet_s_r4_0', 'darknet_m_r4_0', 'darknet_l_r4_0' norm_layer (torchvision.ops): it is recommended to use the default value. For details visit: (https://github.com/facebookresearch/maskrcnn-benchmark/issues/267) pretrained (bool): If True, returns a model with backbone pre-trained on Imagenet - trainable_layers (int): number of trainable (not frozen) resnet layers starting from final block. + trainable_layers (int): number of trainable (not frozen) darknet layers starting from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. version (str): ultralytics release version, currently only supports v3.1 or v4.0 """