iamhankai
commented
6 months ago 是不是dilation参数设置不对，太大了，需要改小点
Open yuan0038 opened 6 months ago
iamhankai
commented
6 months ago 是不是dilation参数设置不对，太大了，需要改小点
yuan0038
commented
6 months ago 目标检测的backbone 如pvig_s，打印出来的dilation是[1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]，模型应该是没问题的。 而且我甚至把dilation全改成1试了试，也还是出现上面的问题,就很神奇。😂
class Pyramid_ViG(torch.nn.Module):
def __init__(self, k,gconv,channels,blocks,n_classes,act,norm,bias,epsilon,use_stochastic,dropout,drop_path,
pretrained=None,out_indices=None):
super().__init__()
self.pretrained = pretrained
self.out_indices = out_indices
self.n_blocks = sum(blocks)
reduce_ratios = [4, 2, 1, 1]
dpr = [x.item() for x in torch.linspace(0, drop_path, self.n_blocks)] # stochastic depth decay rule
num_knn = [int(x.item()) for x in torch.linspace(k, k, self.n_blocks)] # number of knn's k
print(num_knn)
max_dilation = 49 // max(num_knn)
self.stem = Stem(out_dim=channels[0], act=act)
self.pos_embed = nn.Parameter(torch.zeros(1, channels[0], 224 // 4, 224 // 4))
HW = 224 // 4 * 224 // 4
self.backbone = nn.ModuleList([])
#dilation=[min(idx // 4 + 1, max_dilation) for idx in range(sum(blocks))]
dilation = [1 for i in range(sum(blocks))]
idx = 0
for i in range(len(blocks)):
if i > 0:
self.backbone.append(Downsample(channels[i - 1], channels[i]))
HW = HW // 4
for j in range(blocks[i]):
self.backbone += [
Seq(
*[Block(channels[i],num_knn[idx], dilation[idx], gconv, act, norm,
bias, use_stochastic, epsilon, reduce_ratios[i],n=HW, drop_path=dpr[idx],
relative_pos=True)])
]
idx += 1
self.backbone = Seq(*self.backbone)
print("\u2b50 dilation:",dilation)
self.init_weights()
self = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self)
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def init_weights(self):
logger = get_root_logger()
print("Pretrained weights being loaded")
logger.warn('Pretrained weights being loaded')
ckpt_path = self.pretrained
ckpt = _load_checkpoint(
ckpt_path, logger=logger, map_location='cpu')
print("ckpt keys: ", ckpt.keys())
if 'state_dict' in ckpt:
_state_dict = ckpt['state_dict']
elif 'model' in ckpt:
_state_dict = ckpt['model']
else:
_state_dict = ckpt
state_dict = _state_dict
new_state_dict={}
for k,v in state_dict.items():
new_k = k.replace(".grapher",'')
new_state_dict[new_k]=v
print(new_state_dict.keys())
missing_keys, unexpected_keys = \
self.load_state_dict(new_state_dict, False)
print("missing_keys: ", missing_keys)
print("unexpected_keys: ", unexpected_keys)
def interpolate_pos_encoding(self, x):
w, h = x.shape[2], x.shape[3]
p_w, p_h = self.pos_embed.shape[2], self.pos_embed.shape[3]
if w * h == p_w * p_h and w == h:
return self.pos_embed
w0 = w
h0 = h
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
w0, h0 = w0 + 0.1, h0 + 0.1
patch_pos_embed = nn.functional.interpolate(
self.pos_embed,
scale_factor=(w0 / p_w, h0 / p_h),
mode='bicubic',
)
assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
return patch_pos_embed
def forward(self, inputs):
outs=[]
B, C, H, W = inputs.shape
x = self.stem(inputs)
x = x + self.interpolate_pos_encoding(x)
for i in range(len(self.backbone)):
x = self.backbone[i](x)
if i in self.out_indices:
outs.append(x)
return outs def pvig_s_feat(pretrained=True,**kwargs):
model = Pyramid_ViG( k=9, # neighbor num (default:9)
gconv='mr', # graph conv layer {edge, mr}
channels=[80, 160, 400, 640], # number of channels of deep features
blocks=[2, 2, 6, 2], # number of basic blocks in the backbone
n_classes=1000, # Dimension of out_channels
act='gelu', # activation layer {relu, prelu, leakyrelu, gelu, hswish}
norm='batch', # batch or instance normalization {batch, instance}
bias=True, # bias of conv layer True or False
epsilon=0.2, # stochastic epsilon for gcn
use_stochastic=False, # stochastic for gcn, True or False
dropout=0.0, # dropout rate
drop_path=0.0,
pretrained='../ckpt/pvig_s_82.1.pth.tar',
out_indices=[1,4,11,14])
model.default_cfg = _cfg()
return model
韩凯大佬，想请教一下您 PViG中的COCO目标检测配置： 个人环境及配置：
查询过网上相关资料，原因可能是显存不够，然后我尝试
⭐️所以我的问题是，按照标准跑法(虽然batch小了，但是跟标准跑法一样都是平均每张卡都是2张图片），为什么A6000会跑不动PViG_S,恳请大佬指点🌹