diff --git a/configs/vitpose_sam/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTSam_base_coco_256x192.py b/configs/vitpose_sam/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTSam_base_coco_256x192.py new file mode 100644 index 0000000..3f7cf86 --- /dev/null +++ b/configs/vitpose_sam/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTSam_base_coco_256x192.py @@ -0,0 +1,177 @@ +_base_ = [ + '../../../../_base_/default_runtime.py', + '../../../../_base_/datasets/coco.py' +] +evaluation = dict(interval=1, metric='mAP', save_best='AP') + +optimizer = dict(type='AdamW', + lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1, + constructor='LayerDecayOptimizerConstructor', + paramwise_cfg=dict( + num_layers=12, + layer_decay_rate=0.75, + custom_keys={ + 'bias': dict(decay_multi=0.), + 'pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + } + ) + ) + +optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2)) + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[170, 200]) +total_epochs = 210 +target_type = 'GaussianHeatmap' +channel_cfg = dict( + num_output_channels=17, + dataset_joints=17, + dataset_channel=[ + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], + ], + inference_channel=[ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 + ]) + +# model settings +model = dict( + type='TopDownSelf', + pretrained=None, + backbone=dict( + type='ViTSam', + img_size=(256, 192), + patch_size=16, + embed_dim=768, + depth=12, + num_heads=12, + ratio=1, + use_checkpoint=False, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.3, + frozen_stages=12, + freeze_attn = True, + freeze_ffn = True, + samvit_checkpoint='/root/autodl-tmp/code/ViTPose/checkpoints/sam/sam_vit_b_01ec64.pth' + ), + keypoint_head=dict( + type='TopdownHeatmapSimpleHead', + in_channels=768, + num_deconv_layers=2, + num_deconv_filters=(256, 256), + num_deconv_kernels=(4, 4), + extra=dict(final_conv_kernel=1, ), + out_channels=channel_cfg['num_output_channels'], + loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), + train_cfg=dict(), + test_cfg=dict( + flip_test=True, + post_process='default', + shift_heatmap=False, + target_type=target_type, + modulate_kernel=11, + use_udp=True)) + +data_root = '/root/autodl-tmp/dataset/coco2017/' + +data_cfg = dict( + image_size=[192, 256], + heatmap_size=[48, 64], + num_output_channels=channel_cfg['num_output_channels'], + num_joints=channel_cfg['dataset_joints'], + dataset_channel=channel_cfg['dataset_channel'], + inference_channel=channel_cfg['inference_channel'], + soft_nms=False, + nms_thr=1.0, + oks_thr=0.9, + vis_thr=0.2, + use_gt_bbox=False, + det_bbox_thr=0.0, + bbox_file=f'{data_root}/person_detection_results/COCO_val2017_detections_AP_H_56_person.json', +) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='TopDownRandomFlip', flip_prob=0.5), + dict( + type='TopDownHalfBodyTransform', + num_joints_half_body=8, + prob_half_body=0.3), + dict( + type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), + # dict(type='TopDownAffine', use_udp=True), + dict(type='TopDownAffineSam', use_udp=True), + dict(type='ToTensorSam'), + dict( + type='NormalizeTensorSam', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict( + type='TopDownGenerateTarget', + sigma=2, + encoding='UDP', + target_type=target_type), + dict( + type='Collect', + keys=['img', 'sam_img', 'target', 'target_weight'], + meta_keys=[ + 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', + 'rotation', 'bbox_score', 'flip_pairs' + ]), +] + +val_pipeline = [ + dict(type='LoadImageFromFile'), + # dict(type='TopDownAffine', use_udp=True), + dict(type='TopDownAffineSam', use_udp=True), + dict(type='ToTensorSam'), + dict( + type='NormalizeTensorSam', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict( + type='Collect', + keys=['img', 'sam_img'], + meta_keys=[ + 'image_file', 'center', 'scale', 'rotation', 'bbox_score', + 'flip_pairs' + ]), +] + +test_pipeline = val_pipeline + +data = dict( + samples_per_gpu=12, + workers_per_gpu=4, + val_dataloader=dict(samples_per_gpu=12), + test_dataloader=dict(samples_per_gpu=12), + train=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', + img_prefix=f'{data_root}/train2017/', + data_cfg=data_cfg, + pipeline=train_pipeline, + dataset_info={{_base_.dataset_info}}), + val=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', + img_prefix=f'{data_root}/val2017/', + data_cfg=data_cfg, + pipeline=val_pipeline, + dataset_info={{_base_.dataset_info}}), + test=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', + img_prefix=f'{data_root}/val2017/', + data_cfg=data_cfg, + pipeline=test_pipeline, + dataset_info={{_base_.dataset_info}}), +) + diff --git a/mmpose/.mim/configs b/mmpose/.mim/configs new file mode 120000 index 0000000..5992d10 --- /dev/null +++ b/mmpose/.mim/configs @@ -0,0 +1 @@ +../../configs \ No newline at end of file diff --git a/mmpose/.mim/demo b/mmpose/.mim/demo new file mode 120000 index 0000000..bf71256 --- /dev/null +++ b/mmpose/.mim/demo @@ -0,0 +1 @@ +../../demo \ No newline at end of file diff --git a/mmpose/.mim/model-index.yml b/mmpose/.mim/model-index.yml new file mode 120000 index 0000000..a18c0b3 --- /dev/null +++ b/mmpose/.mim/model-index.yml @@ -0,0 +1 @@ +../../model-index.yml \ No newline at end of file diff --git a/mmpose/.mim/tools b/mmpose/.mim/tools new file mode 120000 index 0000000..31941e9 --- /dev/null +++ b/mmpose/.mim/tools @@ -0,0 +1 @@ +../../tools \ No newline at end of file diff --git a/mmpose/datasets/pipelines/__init__.py b/mmpose/datasets/pipelines/__init__.py index cf06db1..db117a3 100644 --- a/mmpose/datasets/pipelines/__init__.py +++ b/mmpose/datasets/pipelines/__init__.py @@ -6,3 +6,6 @@ from .mesh_transform import * # noqa from .pose3d_transform import * # noqa from .shared_transform import * # noqa from .top_down_transform import * # noqa + +from .top_down_transform_self import * # noqa +from .shared_transform_self import * # noqa diff --git a/mmpose/datasets/pipelines/shared_transform_self.py b/mmpose/datasets/pipelines/shared_transform_self.py new file mode 100644 index 0000000..b9faf29 --- /dev/null +++ b/mmpose/datasets/pipelines/shared_transform_self.py @@ -0,0 +1,76 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings +from collections.abc import Sequence + +import mmcv +import numpy as np +from mmcv.parallel import DataContainer as DC +from mmcv.utils import build_from_cfg +from numpy import random +from torchvision.transforms import functional as F + +from ..builder import PIPELINES + +try: + import albumentations +except ImportError: + albumentations = None + + +@PIPELINES.register_module() +class ToTensorSam: + """Transform image to Tensor. + + Required key: 'img'. Modifies key: 'img'. + + Args: + results (dict): contain all information about training. + """ + + def __call__(self, results): + if isinstance(results['img'], (list, tuple)): + results['img'] = [F.to_tensor(img) for img in results['img']] + # 修改 + results['sam_img'] = [F.to_tensor(sam_img) for sam_img in results['sam_img']] + else: + results['img'] = F.to_tensor(results['img']) + # 修改 + results['sam_img'] = F.to_tensor(results['sam_img']) + + return results + + +@PIPELINES.register_module() +class NormalizeTensorSam: + """Normalize the Tensor image (CxHxW), with mean and std. + + Required key: 'img'. Modifies key: 'img'. + + Args: + mean (list[float]): Mean values of 3 channels. + std (list[float]): Std values of 3 channels. + """ + + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, results): + if isinstance(results['img'], (list, tuple)): + results['img'] = [ + F.normalize(img, mean=self.mean, std=self.std) + for img in results['img'] + ] + # 修改 + results['sam_img'] = [ + F.normalize(sam_img, mean=self.mean, std=self.std) + for sam_img in results['sam_img'] + ] + else: + results['img'] = F.normalize( + results['img'], mean=self.mean, std=self.std) + # 修改 + results['sam_img'] = F.normalize( + results['sam_img'], mean=self.mean, std=self.std) + + return results diff --git a/mmpose/datasets/pipelines/top_down_transform_self.py b/mmpose/datasets/pipelines/top_down_transform_self.py new file mode 100644 index 0000000..c6c8b49 --- /dev/null +++ b/mmpose/datasets/pipelines/top_down_transform_self.py @@ -0,0 +1,113 @@ +import cv2 +import numpy as np + +from mmpose.core.post_processing import (affine_transform, fliplr_joints, + get_affine_transform, get_warp_matrix, + warp_affine_joints) +from mmpose.datasets.builder import PIPELINES + +@PIPELINES.register_module() +class TopDownAffineSam: + """Affine transform the image to make input. + + Required keys:'img', 'joints_3d', 'joints_3d_visible', 'ann_info','scale', + 'rotation' and 'center'. + + Modified keys:'img', 'joints_3d', and 'joints_3d_visible'. + + Args: + use_udp (bool): To use unbiased data processing. + Paper ref: Huang et al. The Devil is in the Details: Delving into + Unbiased Data Processing for Human Pose Estimation (CVPR 2020). + """ + + def __init__(self, use_udp=False): + self.use_udp = use_udp + + def __call__(self, results): + image_size = results['ann_info']['image_size'] + # 修改 + sam_image_size = np.array([1024, 1024]) + + img = results['img'] + joints_3d = results['joints_3d'] + joints_3d_visible = results['joints_3d_visible'] + c = results['center'] + s = results['scale'] + r = results['rotation'] + # 修改 + sam_img = img + + if self.use_udp: + trans = get_warp_matrix(r, c * 2.0, image_size - 1.0, s * 200.0) + # 修改 + sam_trans = get_warp_matrix(r, c * 2.0, sam_image_size - 1.0, s * 200.0) + if not isinstance(img, list): + img = cv2.warpAffine( + img, + trans, (int(image_size[0]), int(image_size[1])), + flags=cv2.INTER_LINEAR) + # 修改 + sam_img = cv2.warpAffine( + sam_img, + sam_trans, (int(sam_image_size[0]), int(sam_image_size[1])), + flags=cv2.INTER_LINEAR) + else: + img = [ + cv2.warpAffine( + i, + trans, (int(image_size[0]), int(image_size[1])), + flags=cv2.INTER_LINEAR) for i in img + ] + # 修改 + sam_img = [ + cv2.warpAffine( + i, + sam_trans, (int(sam_image_size[0]), int(sam_image_size[1])), + flags=cv2.INTER_LINEAR) for i in sam_img + ] + + joints_3d[:, 0:2] = \ + warp_affine_joints(joints_3d[:, 0:2].copy(), trans) + + else: + trans = get_affine_transform(c, s, r, image_size) + # 修改 + sam_trans = get_affine_transform(c, s, r, sam_image_size) + if not isinstance(img, list): + img = cv2.warpAffine( + img, + trans, (int(image_size[0]), int(image_size[1])), + flags=cv2.INTER_LINEAR) + + # 修改 + sam_img = cv2.warpAffine( + sam_img, + sam_trans, (int(sam_image_size[0]), int(sam_image_size[1])), + flags=cv2.INTER_LINEAR) + else: + img = [ + cv2.warpAffine( + i, + trans, (int(image_size[0]), int(image_size[1])), + flags=cv2.INTER_LINEAR) for i in img + ] + # 修改 + sam_img = [ + cv2.warpAffine( + i, + sam_trans, (int(sam_image_size[0]), int(sam_image_size[1])), + flags=cv2.INTER_LINEAR) for i in sam_img + ] + + for i in range(results['ann_info']['num_joints']): + if joints_3d_visible[i, 0] > 0.0: + joints_3d[i, + 0:2] = affine_transform(joints_3d[i, 0:2], trans) + + results['img'] = img + results['sam_img'] = sam_img + results['joints_3d'] = joints_3d + results['joints_3d_visible'] = joints_3d_visible + + return results \ No newline at end of file diff --git a/mmpose/models/backbones/__init__.py b/mmpose/models/backbones/__init__.py index 2b8efcf..2697798 100644 --- a/mmpose/models/backbones/__init__.py +++ b/mmpose/models/backbones/__init__.py @@ -27,10 +27,12 @@ from .vipnas_resnet import ViPNAS_ResNet from .vit import ViT from .vit_moe import ViTMoE +from .vit_sam import ViTSam + __all__ = [ 'AlexNet', 'HourglassNet', 'HourglassAENet', 'HRNet', 'MobileNetV2', 'MobileNetV3', 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SCNet', 'SEResNet', 'SEResNeXt', 'ShuffleNetV1', 'ShuffleNetV2', 'CPM', 'RSN', 'MSPN', 'ResNeSt', 'VGG', 'TCN', 'ViPNAS_ResNet', 'ViPNAS_MobileNetV3', - 'LiteHRNet', 'V2VNet', 'HRFormer', 'ViT', 'ViTMoE' + 'LiteHRNet', 'V2VNet', 'HRFormer', 'ViT', 'ViTMoE', 'ViTSam' ] diff --git a/mmpose/models/backbones/sam_vit/__init__.py b/mmpose/models/backbones/sam_vit/__init__.py new file mode 100644 index 0000000..ba70579 --- /dev/null +++ b/mmpose/models/backbones/sam_vit/__init__.py @@ -0,0 +1 @@ +from .image_encoder import build_vit_sam \ No newline at end of file diff --git a/mmpose/models/backbones/sam_vit/image_encoder.py b/mmpose/models/backbones/sam_vit/image_encoder.py new file mode 100644 index 0000000..10e4488 --- /dev/null +++ b/mmpose/models/backbones/sam_vit/image_encoder.py @@ -0,0 +1,477 @@ +# -------------------------------------------------------------------- +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------------------- + +from typing import Optional, Tuple, Type + +import torch +import torch.nn as nn +import torch.nn +import torch.nn.functional as F + +from functools import partial + + +# ---------------------- Vision Transformer of Segment-Anything ---------------------- +class ImageEncoderViT(nn.Module): + """ + We remove the neck which used in the Segment-Anything. + """ + def __init__(self, + img_size : int = 1024, + patch_size : int = 16, + in_chans : int = 3, + embed_dim : int = 768, + depth : int = 12, + num_heads : int = 12, + mlp_ratio : float = 4.0, + qkv_bias : bool = True, + norm_layer : Type[nn.Module] = nn.LayerNorm, + act_layer : Type[nn.Module] = nn.GELU, + use_abs_pos : bool = True, + use_rel_pos : bool = True, + window_size : int = 0, + global_attn_indexes : Tuple[int, ...] = (), + checkpoint = None + ) -> None: + super().__init__() + self.img_size = img_size + self.patch_size = patch_size + self.embed_dim = embed_dim + self.num_patches = (img_size // patch_size) ** 2 + # self.num_patches = (img_size[0] // patch_size) * (img_size[1] // patch_size) + self.pos_embed: Optional[nn.Parameter] = None + self.checkpoint = checkpoint + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) + ) + + # ------------ Model parameters ------------ + ## Patch embedding layer + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + ## ViT blocks + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block(dim = embed_dim, + num_heads = num_heads, + mlp_ratio = mlp_ratio, + qkv_bias = qkv_bias, + norm_layer = norm_layer, + act_layer = act_layer, + use_rel_pos = use_rel_pos, + window_size = window_size if i not in global_attn_indexes else 0, + input_size = (img_size // patch_size, img_size // patch_size), + ) + self.blocks.append(block) + + self.load_pretrained() + + def load_pretrained(self): + if self.checkpoint is not None: + print('Loading SAM pretrained weight from : {}'.format(self.checkpoint)) + # checkpoint state dict + checkpoint_state_dict = torch.load(self.checkpoint, map_location="cpu") + # model state dict + model_state_dict = self.state_dict() + encoder_state_dict = {} + # check + for k in list(checkpoint_state_dict.keys()): + if "image_encoder" in k and k[14:] in model_state_dict: + shape_model = tuple(model_state_dict[k[14:]].shape) + shape_checkpoint = tuple(checkpoint_state_dict[k].shape) + if shape_model == shape_checkpoint or "pos_embed" in k: + encoder_state_dict[k[14:]] = checkpoint_state_dict[k] + else: + print("Shape unmatch: ", k) + + # interpolate position embedding + # interpolate_pos_embed(self, encoder_state_dict, ((self.img_size[0] // self.patch_size), (self.img_size[1] // self.patch_size))) + interpolate_pos_embed(self, encoder_state_dict,) + + # load the weight + self.load_state_dict(encoder_state_dict, strict=False) + else: + print('No SAM pretrained.') + + # @torch.no_grad() + def forward(self, x: torch.Tensor) -> torch.Tensor: + # with torch.no_grad(): + + x = self.patch_embed(x) + if self.pos_embed is not None: + x = x + self.pos_embed + + for blk in self.blocks: + x = blk(x) + + # [B, H, W, C] -> [B, N, C] + return x.flatten(1, 2) + + +# ---------------------- Model modules ---------------------- +class MLPBlock(nn.Module): + def __init__(self, + embedding_dim: int, + mlp_dim: int, + act: Type[nn.Module] = nn.GELU, + ) -> None: + super().__init__() + self.lin1 = nn.Linear(embedding_dim, mlp_dim) + self.lin2 = nn.Linear(mlp_dim, embedding_dim) + self.act = act() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lin2(self.act(self.lin1(x))) + +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + + return x + +class Block(nn.Module): + def __init__(self, + dim : int, + num_heads : int, + mlp_ratio : float = 4.0, + qkv_bias : bool = True, + norm_layer : Type[nn.Module] = nn.LayerNorm, + act_layer : Type[nn.Module] = nn.GELU, + use_rel_pos : bool = False, + window_size : int = 0, + input_size : Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__() + # -------------- Basic parameters -------------- + self.window_size = window_size + # -------------- Model parameters -------------- + self.norm1 = norm_layer(dim) + self.attn = Attention(dim = dim, + num_heads = num_heads, + qkv_bias = qkv_bias, + use_rel_pos = use_rel_pos, + input_size = input_size if window_size == 0 else (window_size, window_size), + ) + self.norm2 = norm_layer(dim) + self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + x + x = x + self.mlp(self.norm2(x)) + + return x + +class Attention(nn.Module): + def __init__(self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = True, + use_rel_pos: bool = False, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + super().__init__() + # -------------- Basic parameters -------------- + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + assert ( + input_size is not None + ), "Input size must be provided if using relative positional encoding." + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + # -------------- Model parameters -------------- + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + attn = (q * self.scale) @ k.transpose(-2, -1) + + if self.use_rel_pos: + attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + attn = attn.softmax(dim=-1) + x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + x = self.proj(x) + + return x + +class PatchEmbed(nn.Module): + def __init__(self, + kernel_size : Tuple[int, int] = (16, 16), + stride : Tuple[int, int] = (16, 16), + padding : Tuple[int, int] = (0, 0), + in_chans : int = 3, + embed_dim : int = 768, + ) -> None: + super().__init__() + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # [B, C, H, W] -> [B, H, W, C] + x = x.permute(0, 2, 3, 1) + + return x + + +# ---------------------- Model functions ---------------------- +def window_partition(x: torch.Tensor, + window_size: int, + ) -> Tuple[torch.Tensor, Tuple[int, int]]: + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + + return windows, (Hp, Wp) + +def window_unpartition(windows: torch.Tensor, + window_size: int, + pad_hw: Tuple[int, int], + hw: Tuple[int, int], + ) -> torch.Tensor: + """ + Window unpartition into original sequences and removing padding. + Args: + windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + + return x + +def get_rel_pos(q_size: int, + k_size: int, + rel_pos: torch.Tensor, + )-> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + +def add_decomposed_rel_pos(attn : torch.Tensor, + q : torch.Tensor, + rel_pos_h : torch.Tensor, + rel_pos_w : torch.Tensor, + q_size : Tuple[int, int], + k_size : Tuple[int, int], + ) -> torch.Tensor: + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + + attn = ( + attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + ).view(B, q_h * q_w, k_h * k_w) + + return attn + +def interpolate_pos_embed(model, checkpoint_model): + if 'pos_embed' in checkpoint_model: + # Pos embed from checkpoint + pos_embed_checkpoint = checkpoint_model['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + # Pos embed from model + pos_embed_model = model.pos_embed + num_patches = model.num_patches + # [B, H, W, C] -> [B, N, C] + pos_embed_checkpoint = pos_embed_checkpoint.flatten(1, 2) + pos_embed_model = pos_embed_model.flatten(1, 2) + + orig_num_postions = pos_embed_model.shape[-2] + num_extra_tokens = orig_num_postions - num_patches + + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + new_size = int(num_patches ** 0.5) + + # height (== width) for the new position embedding + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("- Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate(pos_tokens, + size=(new_size,new_size), + mode='bicubic', + align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + new_pos_embed = new_pos_embed.reshape(-1, int(orig_num_postions ** 0.5), int(orig_num_postions ** 0.5), embedding_size) + checkpoint_model['pos_embed'] = new_pos_embed + + +# ------------------------ Model Functions ------------------------ +def build_vit_sam(model_name="vit_h", img_size=1024, patch_size=16, img_dim=3, checkpoint=None): + if model_name == "vit_b": + return ImageEncoderViT(img_size=img_size, + patch_size=patch_size, + in_chans=img_dim, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + global_attn_indexes=[2, 5, 8, 11], + window_size=14, + checkpoint=checkpoint, + ) + if model_name == "vit_l": + return ImageEncoderViT(img_size=img_size, + patch_size=patch_size, + in_chans=img_dim, + embed_dim=1024, + depth=24, + num_heads=16, + mlp_ratio=4.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + global_attn_indexes=[5, 11, 17, 23], + window_size=14, + checkpoint=checkpoint, + ) + if model_name == "vit_h": + return ImageEncoderViT(img_size=img_size, + patch_size=patch_size, + in_chans=img_dim, + embed_dim=1280, + depth=32, + num_heads=16, + mlp_ratio=4.0, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + global_attn_indexes=[7, 15, 23, 31], + window_size=14, + checkpoint=checkpoint, + ) + + +if __name__ == '__main__': + import torch + from thop import profile + + # Prepare an image as the input + bs, c, h, w = 2, 3, 1024, 1024 + x = torch.randn(bs, c, h, w) + patch_size = 16 + device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu') + + # Build model + model = build_vit_sam(model_name='vit_b', checkpoint="/home/fhw/code/ViTPose/checkpoints/sam/sam_vit_b_01ec64.pth") + if torch.cuda.is_available(): + x = x.to(device) + model = model.to(device) + + # Inference + outputs = model(x) + print(outputs.shape) + + # Compute FLOPs & Params + print('==============================') + model.eval() + flops, params = profile(model, inputs=(x, ), verbose=False) + print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2)) + print('Params : {:.2f} M'.format(params / 1e6)) diff --git a/mmpose/models/backbones/vit_sam.py b/mmpose/models/backbones/vit_sam.py new file mode 100644 index 0000000..80c1b4f --- /dev/null +++ b/mmpose/models/backbones/vit_sam.py @@ -0,0 +1,483 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import torch +from functools import partial +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ + +from ..builder import BACKBONES +from .base_backbone import BaseBackbone + +from .sam_vit import build_vit_sam + + +def get_abs_pos(abs_pos, h, w, ori_h, ori_w, has_cls_token=True): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token + dimension for the original embeddings. + Args: + abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). + has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. + hw (Tuple): size of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, H, W, C) + """ + cls_token = None + B, L, C = abs_pos.shape + if has_cls_token: + cls_token = abs_pos[:, 0:1] + abs_pos = abs_pos[:, 1:] + + if ori_h != h or ori_w != w: + new_abs_pos = F.interpolate( + abs_pos.reshape(1, ori_h, ori_w, -1).permute(0, 3, 1, 2), + size=(h, w), + mode="bicubic", + align_corners=False, + ).permute(0, 2, 3, 1).reshape(B, -1, C) + + else: + new_abs_pos = abs_pos + + if cls_token is not None: + new_abs_pos = torch.cat([cls_token, new_abs_pos], dim=1) + return new_abs_pos + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self): + return 'p={}'.format(self.drop_prob) + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., attn_head_dim=None,): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.dim = dim + + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + + return x + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, + drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, + norm_layer=nn.LayerNorm, attn_head_dim=None + ): + super().__init__() + + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, attn_head_dim=attn_head_dim + ) + + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (ratio ** 2) + self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio)) + self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(img_size[1] // patch_size[1])) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=(patch_size[0] // ratio), padding=4 + 2 * (ratio//2-1)) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + x = self.proj(x) + Hp, Wp = x.shape[2], x.shape[3] + + x = x.flatten(2).transpose(1, 2) + return x, (Hp, Wp) + + +class HybridEmbed(nn.Module): + """ CNN Feature Map Embedding + Extract feature map from CNN, flatten, project to embedding dim. + """ + def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): + super().__init__() + assert isinstance(backbone, nn.Module) + img_size = to_2tuple(img_size) + self.img_size = img_size + self.backbone = backbone + if feature_size is None: + with torch.no_grad(): + training = backbone.training + if training: + backbone.eval() + o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1] + feature_size = o.shape[-2:] + feature_dim = o.shape[1] + backbone.train(training) + else: + feature_size = to_2tuple(feature_size) + feature_dim = self.backbone.feature_info.channels()[-1] + self.num_patches = feature_size[0] * feature_size[1] + self.proj = nn.Linear(feature_dim, embed_dim) + + def forward(self, x): + x = self.backbone(x)[-1] + x = x.flatten(2).transpose(1, 2) + x = self.proj(x) + return x + +class Cross_Attention(nn.Module): + def __init__(self, dim, num_heads=12, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.self_attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=0.) + + self.linear_q = nn.Linear(dim, dim, bias=qkv_bias) + self.linear_k = nn.Linear(dim, dim, bias=qkv_bias) + self.linear_v = nn.Linear(dim, dim, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x_1, x_2, x_3): + B, N, C = x_1.shape # q + B, N_1, C = x_2.shape # k, v + + q = self.linear_q(x_1).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # (B, num_heads, N, C//num_heads) + k = self.linear_k(x_2).reshape(B, N_1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # (B, num_heads, N_1, C//num_heads) + v = self.linear_v(x_3).reshape(B, N_1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # (B, num_heads, N_1, C//num_heads) + + attn = (q @ k.transpose(-2, -1)) * self.scale # (B, num_heads, N, N_1) + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + # import matplotlib.pyplot as plt + # import seaborn as sns + + # attn_map = attn[0][0].cpu().detach().numpy() + # plt.figure(figsize=(20, 10)) + # sns.heatmap(attn_map, annot=True, fmt='.2f', cmap='coolwarm') + + # plt.title('Cross Attention Map') + # plt.xlabel('N_1') + # plt.ylabel('N') + + # plt.savefig('/home/fhw/code/ViTPose/test/cross_attn_map.png') + # plt.close() + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) # (B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + +class CustomAttentionFFN(nn.Module): + def __init__(self, dim, num_heads=12, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.self_attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=proj_drop) + + self.cross_attn = Cross_Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, \ + qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=proj_drop) + + self.ffn = nn.Sequential( + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Linear(dim * 4, dim) + ) + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + + def forward(self, x1, x2): + x1 = self.norm1(x1 + self.self_attn(x1)) + + x1 = self.norm2(x1 + self.cross_attn(x1, x2, x2)) + + x1 = self.norm3(x1 + self.ffn(x1)) + + return x1 + +@BACKBONES.register_module() +class ViTSam(BaseBackbone): + + def __init__(self, + img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., hybrid_backbone=None, norm_layer=None, use_checkpoint=False, + frozen_stages=-1, ratio=1, last_norm=True, + patch_padding='pad', freeze_attn=False, freeze_ffn=False, samvit_checkpoint=None + ): + # Protect mutable default arguments + super(ViTSam, self).__init__() + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.frozen_stages = frozen_stages + self.use_checkpoint = use_checkpoint + self.patch_padding = patch_padding + self.freeze_attn = freeze_attn + self.freeze_ffn = freeze_ffn + self.depth = depth + + if hybrid_backbone is not None: + self.patch_embed = HybridEmbed( + hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) + else: + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ratio=ratio) + num_patches = self.patch_embed.num_patches + + # since the pretraining model has class token + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + ) + for i in range(depth)]) + + self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + self._freeze_stages() + + # ======================== SAM-ViT ======================== + self.sam_vit = build_vit_sam(model_name='vit_b', checkpoint=samvit_checkpoint) + self.sam_vit.eval() + for param in self.sam_vit.parameters(): + param.requires_grad = False + + # self.cross_attn = Cross_Attention(embed_dim, num_heads=num_heads, qkv_bias=qkv_bias, \ + # qk_scale=qk_scale, attn_drop=attn_drop_rate, proj_drop=drop_rate) + + self.custom_attn_ffn = CustomAttentionFFN(embed_dim, num_heads=num_heads, qkv_bias=qkv_bias, \ + qk_scale=qk_scale, attn_drop=attn_drop_rate, proj_drop=drop_rate) + + def _freeze_stages(self): + """Freeze parameters.""" + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + for i in range(0, self.frozen_stages): + m = self.blocks[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + if self.freeze_attn: + for i in range(0, self.depth): + m = self.blocks[i] + m.attn.eval() + m.norm1.eval() + for param in m.attn.parameters(): + param.requires_grad = False + for param in m.norm1.parameters(): + param.requires_grad = False + + if self.freeze_ffn: + self.pos_embed.requires_grad = False + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + for i in range(0, self.depth): + m = self.blocks[i] + m.mlp.eval() + m.norm2.eval() + for param in m.mlp.parameters(): + param.requires_grad = False + for param in m.norm2.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + super().init_weights(pretrained, patch_padding=self.patch_padding) + + if pretrained is None: + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward_features(self, x): + B, C, H, W = x.shape + x, (Hp, Wp) = self.patch_embed(x) + + if self.pos_embed is not None: + # fit for multiple GPU training + # since the first element for pos embed (sin-cos manner) is zero, it will cause no difference + x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1] + + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + + x = self.last_norm(x) + + return x, Hp, Wp + + def forward(self, x1, x2): + import time + B, _, _, _ = x1.shape + x1, Hp, Wp = self.forward_features(x1) # B, N_vitpose, C + + with torch.no_grad(): + # start_time = time.time() + # self.sam_vit.eval() + x2 = self.sam_vit(x2) # B, N_sam, C + + # end_time = time.time() + # print('SAM-ViT forward time: {:.4f}秒'.format(end_time - start_time)) + + # x1 = x1 + self.cross_attn(x1, x2, x2) + + x1 = self.custom_attn_ffn(x1, x2) + + xp = x1.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous() # B, C, Hp, Wp + return xp + + def train(self, mode=True): + """Convert the model into training mode.""" + super().train(mode) + self._freeze_stages() + + +if __name__ == '__main__': + from thop import profile + from mmcv.runner import load_checkpoint + + # Prepare an image as the input + bs, c, h, w = 2, 3, 1024, 1024 + x1 = torch.randn(bs, c, 256, 192) + x2 = torch.randn(bs, c, h, w) + patch_size = 16 + device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu') + + # Build model + model = ViTSam(img_size=(256, 192), patch_size=16, embed_dim=768, depth=12, num_heads=12, ratio=1, + use_checkpoint=False, mlp_ratio=4, qkv_bias=True, drop_path_rate=0.3, + samvit_checkpoint='/home/fhw/code/ViTPose/checkpoints/sam/sam_vit_b_01ec64.pth') + + + if torch.cuda.is_available(): + x1 = x1.to(device) + x2 = x2.to(device) + model = model.to(device) + + with torch.no_grad(): + model.eval() + # Inference + outputs = model(x1, x2) + print(outputs.shape) + + # Compute FLOPs & Params + print('==============================') + model.eval() + flops, params = profile(model, inputs=(x1, x2), verbose=False) + print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2)) + print('Params : {:.2f} M'.format(params / 1e6)) diff --git a/mmpose/models/detectors/__init__.py b/mmpose/models/detectors/__init__.py index e098209..9da32ce 100644 --- a/mmpose/models/detectors/__init__.py +++ b/mmpose/models/detectors/__init__.py @@ -10,8 +10,10 @@ from .posewarper import PoseWarper from .top_down import TopDown from .top_down_moe import TopDownMoE +from .top_down_self import TopDownSelf + __all__ = [ 'TopDown', 'AssociativeEmbedding', 'ParametricMesh', 'MultiTask', 'PoseLifter', 'Interhand3D', 'PoseWarper', 'DetectAndRegress', - 'VoxelCenterDetector', 'VoxelSinglePose', 'TopDownMoE' + 'VoxelCenterDetector', 'VoxelSinglePose', 'TopDownMoE', 'TopDownSelf' ] diff --git a/mmpose/models/detectors/top_down_self.py b/mmpose/models/detectors/top_down_self.py new file mode 100644 index 0000000..d96a4d9 --- /dev/null +++ b/mmpose/models/detectors/top_down_self.py @@ -0,0 +1,322 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings +import logging + +import mmcv +import numpy as np +from mmcv.image import imwrite +from mmcv.utils.misc import deprecated_api_warning +from mmcv.visualization.image import imshow +from mmcv_custom.checkpoint import load_checkpoint + +from mmpose.core import imshow_bboxes, imshow_keypoints +from .. import builder +from ..builder import POSENETS +from .base import BasePose + +try: + from mmcv.runner import auto_fp16 +except ImportError: + warnings.warn('auto_fp16 from mmpose will be deprecated from v0.15.0' + 'Please install mmcv>=1.1.4') + from mmpose.core import auto_fp16 + + +@POSENETS.register_module() +class TopDownSelf(BasePose): + """Top-down pose detectors. + + Args: + backbone (dict): Backbone modules to extract feature. + keypoint_head (dict): Keypoint head to process feature. + train_cfg (dict): Config for training. Default: None. + test_cfg (dict): Config for testing. Default: None. + pretrained (str): Path to the pretrained models. + loss_pose (None): Deprecated arguments. Please use + `loss_keypoint` for heads instead. + """ + + def __init__(self, + backbone, + neck=None, + keypoint_head=None, + train_cfg=None, + test_cfg=None, + pretrained=None, + loss_pose=None): + super().__init__() + self.fp16_enabled = False + + self.backbone = builder.build_backbone(backbone) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + if neck is not None: + self.neck = builder.build_neck(neck) + + if keypoint_head is not None: + keypoint_head['train_cfg'] = train_cfg + keypoint_head['test_cfg'] = test_cfg + + if 'loss_keypoint' not in keypoint_head and loss_pose is not None: + warnings.warn( + '`loss_pose` for TopDown is deprecated, ' + 'use `loss_keypoint` for heads instead. See ' + 'https://github.com/open-mmlab/mmpose/pull/382' + ' for more information.', DeprecationWarning) + keypoint_head['loss_keypoint'] = loss_pose + + self.keypoint_head = builder.build_head(keypoint_head) + + self.init_weights(pretrained=pretrained) + + @property + def with_neck(self): + """Check if has neck.""" + return hasattr(self, 'neck') + + @property + def with_keypoint(self): + """Check if has keypoint_head.""" + return hasattr(self, 'keypoint_head') + + def init_weights(self, pretrained=None): + """Weight initialization for model.""" + self.backbone.init_weights(pretrained) + if self.with_neck: + self.neck.init_weights() + if self.with_keypoint: + self.keypoint_head.init_weights() + + @auto_fp16(apply_to=('img', 'sam_img', )) + def forward(self, + img, + sam_img, # 针对sam_encoder的输入 + target=None, + target_weight=None, + img_metas=None, + return_loss=True, + return_heatmap=False, + **kwargs): + """Calls either forward_train or forward_test depending on whether + return_loss=True. Note this setting will change the expected inputs. + When `return_loss=True`, img and img_meta are single-nested (i.e. + Tensor and List[dict]), and when `resturn_loss=False`, img and img_meta + should be double nested (i.e. List[Tensor], List[List[dict]]), with + the outer list indicating test time augmentations. + + Note: + - batch_size: N + - num_keypoints: K + - num_img_channel: C (Default: 3) + - img height: imgH + - img width: imgW + - heatmaps height: H + - heatmaps weight: W + + Args: + img (torch.Tensor[NxCximgHximgW]): Input images. + target (torch.Tensor[NxKxHxW]): Target heatmaps. + target_weight (torch.Tensor[NxKx1]): Weights across + different joint types. + img_metas (list(dict)): Information about data augmentation + By default this includes: + + - "image_file: path to the image file + - "center": center of the bbox + - "scale": scale of the bbox + - "rotation": rotation of the bbox + - "bbox_score": score of bbox + return_loss (bool): Option to `return loss`. `return loss=True` + for training, `return loss=False` for validation & test. + return_heatmap (bool) : Option to return heatmap. + + Returns: + dict|tuple: if `return loss` is true, then return losses. \ + Otherwise, return predicted poses, boxes, image paths \ + and heatmaps. + """ + if return_loss: + # 可视化 img, sam_img cv可视化/PIL Image + # print(sam_img[0].shape) + # imshow(sam_img[0].cpu().numpy().transpose(1, 2, 0), wait_time=5000) + # 修改 + return self.forward_train(img, sam_img, target, target_weight, img_metas, + **kwargs) + # 修改 + return self.forward_test( + img, sam_img, img_metas, return_heatmap=return_heatmap, **kwargs) + + # 修改 + def forward_train(self, img, sam_img, target, target_weight, img_metas, **kwargs): + """Defines the computation performed at every call when training.""" + # 修改 + output = self.backbone(img, sam_img) # B, C, Hp, Wp + if self.with_neck: + output = self.neck(output) + if self.with_keypoint: + output = self.keypoint_head(output) + + # if return loss + losses = dict() + if self.with_keypoint: + keypoint_losses = self.keypoint_head.get_loss( + output, target, target_weight) + losses.update(keypoint_losses) + keypoint_accuracy = self.keypoint_head.get_accuracy( + output, target, target_weight) + losses.update(keypoint_accuracy) + + return losses + + # 修改 + def forward_test(self, img, sam_img, img_metas, return_heatmap=False, **kwargs): + """Defines the computation performed at every call when testing.""" + assert img.size(0) == len(img_metas) + batch_size, _, img_height, img_width = img.shape + if batch_size > 1: + assert 'bbox_id' in img_metas[0] + + result = {} + + # 修改 + features = self.backbone(img, sam_img) + if self.with_neck: + features = self.neck(features) + if self.with_keypoint: + output_heatmap = self.keypoint_head.inference_model( + features, flip_pairs=None) + + if self.test_cfg.get('flip_test', True): + img_flipped = img.flip(3) + # 修改 + sam_img_flipped = sam_img.flip(3) + features_flipped = self.backbone(img_flipped, sam_img_flipped) + if self.with_neck: + features_flipped = self.neck(features_flipped) + if self.with_keypoint: + output_flipped_heatmap = self.keypoint_head.inference_model( + features_flipped, img_metas[0]['flip_pairs']) + output_heatmap = (output_heatmap + + output_flipped_heatmap) * 0.5 + + if self.with_keypoint: + keypoint_result = self.keypoint_head.decode( + img_metas, output_heatmap, img_size=[img_width, img_height]) + result.update(keypoint_result) + + if not return_heatmap: + output_heatmap = None + + result['output_heatmap'] = output_heatmap + + return result + + # 修改 + def forward_dummy(self, img, sam_img): + """Used for computing network FLOPs. + + See ``tools/get_flops.py``. + + Args: + img (torch.Tensor): Input image. + + Returns: + Tensor: Output heatmaps. + """ + output = self.backbone(img, sam_img) + if self.with_neck: + output = self.neck(output) + if self.with_keypoint: + output = self.keypoint_head(output) + return output + + @deprecated_api_warning({'pose_limb_color': 'pose_link_color'}, + cls_name='TopDown') + def show_result(self, + img, + result, + skeleton=None, + kpt_score_thr=0.3, + bbox_color='green', + pose_kpt_color=None, + pose_link_color=None, + text_color='white', + radius=4, + thickness=1, + font_scale=0.5, + bbox_thickness=1, + win_name='', + show=False, + show_keypoint_weight=False, + wait_time=0, + out_file=None): + """Draw `result` over `img`. + + Args: + img (str or Tensor): The image to be displayed. + result (list[dict]): The results to draw over `img` + (bbox_result, pose_result). + skeleton (list[list]): The connection of keypoints. + skeleton is 0-based indexing. + kpt_score_thr (float, optional): Minimum score of keypoints + to be shown. Default: 0.3. + bbox_color (str or tuple or :obj:`Color`): Color of bbox lines. + pose_kpt_color (np.array[Nx3]`): Color of N keypoints. + If None, do not draw keypoints. + pose_link_color (np.array[Mx3]): Color of M links. + If None, do not draw links. + text_color (str or tuple or :obj:`Color`): Color of texts. + radius (int): Radius of circles. + thickness (int): Thickness of lines. + font_scale (float): Font scales of texts. + win_name (str): The window name. + show (bool): Whether to show the image. Default: False. + show_keypoint_weight (bool): Whether to change the transparency + using the predicted confidence scores of keypoints. + wait_time (int): Value of waitKey param. + Default: 0. + out_file (str or None): The filename to write the image. + Default: None. + + Returns: + Tensor: Visualized img, only if not `show` or `out_file`. + """ + img = mmcv.imread(img) + img = img.copy() + + bbox_result = [] + bbox_labels = [] + pose_result = [] + for res in result: + if 'bbox' in res: + bbox_result.append(res['bbox']) + bbox_labels.append(res.get('label', None)) + pose_result.append(res['keypoints']) + + if bbox_result: + bboxes = np.vstack(bbox_result) + # draw bounding boxes + imshow_bboxes( + img, + bboxes, + labels=bbox_labels, + colors=bbox_color, + text_color=text_color, + thickness=bbox_thickness, + font_scale=font_scale, + show=False) + + if pose_result: + imshow_keypoints(img, pose_result, skeleton, kpt_score_thr, + pose_kpt_color, pose_link_color, radius, + thickness) + + if show: + imshow(img, win_name, wait_time) + + if out_file is not None: + imwrite(img, out_file) + + return img diff --git a/test.py b/test.py new file mode 100644 index 0000000..fe0a338 --- /dev/null +++ b/test.py @@ -0,0 +1,17 @@ +import torch +import numpy as np + +model_1 = torch.load('/home/fhw/code/ViTPose/checkpoints/sam/sam_vit_b_01ec64.pth') +model_2 = torch.load('/home/fhw/code/ViTPose/work_dirs/ViTSam_base_coco_256x192/best_AP_epoch_1.pth') + +param_1 = model_1['image_encoder.pos_embed'].numpy() +param_2 = model_2['state_dict']['backbone.sam_vit.pos_embed'].numpy() + +# for name, param in model_2.items(): +# print(name) + +# print(model_2['state_dict']['backbone.sam_vit.pos_embed']) + +is_equal = np.array_equal(param_1, param_2) + +print(is_equal) \ No newline at end of file diff --git a/tools/train+sam.py b/tools/train+sam.py new file mode 100644 index 0000000..592c230 --- /dev/null +++ b/tools/train+sam.py @@ -0,0 +1,197 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import copy +import os +import os.path as osp +import time +import warnings + +import mmcv +import torch +from mmcv import Config, DictAction +from mmcv.runner import get_dist_info, init_dist, set_random_seed, load_checkpoint +from mmcv.utils import get_git_hash + +from mmpose import __version__ +from mmpose.apis import init_random_seed, train_model +from mmpose.datasets import build_dataset +from mmpose.models import build_posenet +from mmpose.utils import collect_env, get_root_logger, setup_multi_processes +import mmcv_custom + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a pose model') + parser.add_argument('config', help='train config file path') + parser.add_argument('-c', '--checkpoint', help='checkpoint file', default='/root/autodl-tmp/code/ViTPose/checkpoints/vitpose/vitpose-b.pth') + parser.add_argument('--work-dir', help='the dir to save logs and models') + parser.add_argument( + '--resume-from', help='the checkpoint file to resume from') + parser.add_argument( + '--no-validate', + action='store_true', + help='whether not to evaluate the checkpoint during training') + group_gpus = parser.add_mutually_exclusive_group() + group_gpus.add_argument( + '--gpus', + type=int, + help='(Deprecated, please use --gpu-id) number of gpus to use ' + '(only applicable to non-distributed training)') + group_gpus.add_argument( + '--gpu-ids', + type=int, + nargs='+', + help='(Deprecated, please use --gpu-id) ids of gpus to use ' + '(only applicable to non-distributed training)') + group_gpus.add_argument( + '--gpu-id', + type=int, + default=0, + help='id of gpu to use ' + '(only applicable to non-distributed training)') + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--deterministic', + action='store_true', + help='whether to set deterministic options for CUDNN backend.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + default={}, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. For example, ' + "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'") + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument( + '--autoscale-lr', + action='store_true', + help='automatically scale lr with the number of gpus') + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + # set multi-process settings + setup_multi_processes(cfg) + + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + + # work_dir is determined in this priority: CLI > segment in file > filename + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(args.config))[0]) + if args.resume_from is not None: + cfg.resume_from = args.resume_from + if args.gpus is not None: + cfg.gpu_ids = range(1) + warnings.warn('`--gpus` is deprecated because we only support ' + 'single GPU mode in non-distributed training. ' + 'Use `gpus=1` now.') + if args.gpu_ids is not None: + cfg.gpu_ids = args.gpu_ids[0:1] + warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. ' + 'Because we only support single GPU mode in ' + 'non-distributed training. Use the first GPU ' + 'in `gpu_ids` now.') + if args.gpus is None and args.gpu_ids is None: + cfg.gpu_ids = [args.gpu_id] + + if args.autoscale_lr: + # apply the linear scaling rule (https://arxiv.org/abs/1706.02677) + cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8 + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + if len(cfg.gpu_ids) > 1: + warnings.warn( + f'We treat {cfg.gpu_ids} as gpu-ids, and reset to ' + f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in ' + 'non-distribute training time.') + cfg.gpu_ids = cfg.gpu_ids[0:1] + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + # re-set gpu_ids with distributed training mode + _, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # init the logger before other steps + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + + # init the meta dict to record some important information such as + # environment info and seed, which will be logged + meta = dict() + # log env info + env_info_dict = collect_env() + env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + meta['env_info'] = env_info + + # log some basic info + logger.info(f'Distributed training: {distributed}') + logger.info(f'Config:\n{cfg.pretty_text}') + + # set random seeds + seed = init_random_seed(args.seed) + logger.info(f'Set random seed to {seed}, ' + f'deterministic: {args.deterministic}') + set_random_seed(seed, deterministic=args.deterministic) + cfg.seed = seed + meta['seed'] = seed + + model = build_posenet(cfg.model) + load_checkpoint(model, args.checkpoint, map_location='cpu') + datasets = [build_dataset(cfg.data.train)] + + if len(cfg.workflow) == 2: + val_dataset = copy.deepcopy(cfg.data.val) + val_dataset.pipeline = cfg.data.train.pipeline + datasets.append(build_dataset(val_dataset)) + + if cfg.checkpoint_config is not None: + # save mmpose version, config file content + # checkpoints as meta data + cfg.checkpoint_config.meta = dict( + mmpose_version=__version__ + get_git_hash(digits=7), + config=cfg.pretty_text, + ) + train_model( + model, + datasets, + cfg, + distributed=distributed, + validate=(not args.no_validate), + timestamp=timestamp, + meta=meta) + + +if __name__ == '__main__': + main() diff --git a/work_dirs/ViTSam_base_coco_256x192/20240706_095205.log.json b/work_dirs/ViTSam_base_coco_256x192/20240706_095205.log.json new file mode 100644 index 0000000..406182f --- /dev/null +++ b/work_dirs/ViTSam_base_coco_256x192/20240706_095205.log.json @@ -0,0 +1,1440 @@ +{"env_info": "sys.platform: linux\nPython: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda-11.3\nNVCC: Build cuda_11.3.r11.3/compiler.29920130_0\nGCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\nPyTorch: 1.7.1+cu110\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n - OpenMP 201511 (a.k.a. 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100644 index 0000000..e8b7d29 --- /dev/null +++ b/遇到的问题.txt @@ -0,0 +1 @@ +安装环境时的问题：由于setuptools版本过高，导致算法使用的安装方式已经被弃用，建议选择重新安装小于60的版本 \ No newline at end of file