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Fix configs and functions for distributed rank checks #111

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58a173c
Fix configs
int11 Aug 21, 2025
d07fa5e
Fix HGNetv2 model loading to use utility functions for distributed ra…
int11 Aug 21, 2025
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2 changes: 1 addition & 1 deletion configs/base/dfine_hgnetv2.yml
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Expand Up @@ -22,7 +22,7 @@ no_aug_epoch: 0

HGNetv2:
pretrained: True
local_model_dir: ../RT-DETR-main/D-FINE/weight/hgnetv2/
local_model_dir: ./weight/hgnetv2/

HybridEncoder:
in_channels: [512, 1024, 2048]
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2 changes: 1 addition & 1 deletion configs/deim_dfine/deim_hgnetv2_n_coco.yml
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Expand Up @@ -3,7 +3,7 @@ __include__: [
'../base/deim.yml'
]

output_dir: ./deim_outputs/deim_hgnetv2_n_coco
output_dir: ./outputs/deim_hgnetv2_n_coco

optimizer:
type: AdamW
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6 changes: 1 addition & 5 deletions configs/deim_dfine/dfine_hgnetv2_n_coco.yml
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Expand Up @@ -68,15 +68,11 @@ optimizer:
# Increase to search for the optimal ema
epoches: 160 # 148 + 4n
train_dataloader:
total_batch_size: 128
dataset:
transforms:
policy:
epoch: 148
collate_fn:
stop_epoch: 148
ema_restart_decay: 0.9999
base_size_repeat: ~

val_dataloader:
total_batch_size: 256
base_size_repeat: ~
8 changes: 5 additions & 3 deletions engine/backbone/hgnetv2.py
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Expand Up @@ -12,6 +12,7 @@
from .common import FrozenBatchNorm2d
from ..core import register
import logging
from ..misc.dist_utils import get_rank, is_dist_available_and_initialized

# Constants for initialization
kaiming_normal_ = nn.init.kaiming_normal_
Expand Down Expand Up @@ -495,11 +496,12 @@ def __init__(self,
print(f"Loaded stage1 {name} HGNetV2 from local file.")
else:
# If the file doesn't exist locally, download from the URL
if torch.distributed.get_rank() == 0:
if get_rank() == 0:
print(GREEN + "If the pretrained HGNetV2 can't be downloaded automatically. Please check your network connection." + RESET)
print(GREEN + "Please check your network connection. Or download the model manually from " + RESET + f"{download_url}" + GREEN + " to " + RESET + f"{local_model_dir}." + RESET)
state = torch.hub.load_state_dict_from_url(download_url, map_location='cpu', model_dir=local_model_dir)
torch.distributed.barrier()
if is_dist_available_and_initialized():
torch.distributed.barrier()
else:
torch.distributed.barrier()
state = torch.load(local_model_dir)
Expand All @@ -509,7 +511,7 @@ def __init__(self,
self.load_state_dict(state)

except (Exception, KeyboardInterrupt) as e:
if torch.distributed.get_rank() == 0:
if get_rank() == 0:
print(f"{str(e)}")
logging.error(RED + "CRITICAL WARNING: Failed to load pretrained HGNetV2 model" + RESET)
logging.error(GREEN + "Please check your network connection. Or download the model manually from " \
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