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config : configs/updown_nocaps_val.yaml
config_override : []
gpu_ids : [0]
cpu_workers : 0
in_memory : False
checkpoint_path : checkpoints/updown.pth
output_path : /results/predictions.json
evalai_submit : False
Traceback (most recent call last):
File "scripts/inference.py", line 117, in
model.load_state_dict(torch.load(_A.checkpoint_path)["model"])
File "/root/anaconda3/envs/updown/lib/python3.6/site-packages/torch/nn/modules/module.py", line 777, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for UpDownCaptioner:
size mismatch for _embedding_layer.weight: copying a param with shape torch.Size([10315, 1000]) from checkpoint, the shape in current model is torch.Size([10306, 1000]).
size mismatch for _output_layer.weight: copying a param with shape torch.Size([10315, 1200]) from checkpoint, the shape in current model is torch.Size([10306, 1200]).
size mismatch for _output_layer.bias: copying a param with shape torch.Size([10315]) from checkpoint, the shape in current model is torch.Size([10306]).
The text was updated successfully, but these errors were encountered:
First of all thank you for your great work and for providing the code! I get the same error as @Mas-Y. Seems like you had a different sized vocabulary. Do you have an idea where I should first look for the error?
python scripts/inference.py --config configs/updown_nocaps_val.yaml --checkpoint-path checkpoints/updown.pth --output-path /results/predictions.json --gpu-ids 0
RANDOM_SEED: 0
DATA:
CBS:
CLASS_HIERARCHY: data/cbs/class_hierarchy.json
INFER_BOXES: data/nocaps_val_oi_detector_boxes.json
MAX_GIVEN_CONSTRAINTS: 3
MAX_WORDS_PER_CONSTRAINT: 3
NMS_THRESHOLD: 0.85
WORDFORMS: data/cbs/constraint_wordforms.tsv
INFER_CAPTIONS: data/nocaps/nocaps_val_image_info.json
INFER_FEATURES: data/nocaps_val_vg_detector_features_adaptive.h5
MAX_CAPTION_LENGTH: 20
TRAIN_CAPTIONS: data/coco/captions_train2017.json
TRAIN_FEATURES: data/coco_train2017_vg_detector_features_adaptive.h5
VOCABULARY: data/vocabulary
MODEL:
ATTENTION_PROJECTION_SIZE: 768
BEAM_SIZE: 5
EMBEDDING_SIZE: 1000
HIDDEN_SIZE: 1200
IMAGE_FEATURE_SIZE: 2048
MIN_CONSTRAINTS_TO_SATISFY: 2
USE_CBS: False
OPTIM:
BATCH_SIZE: 150
CLIP_GRADIENTS: 12.5
LR: 0.015
MOMENTUM: 0.9
NUM_ITERATIONS: 70000
WEIGHT_DECAY: 0.001
config : configs/updown_nocaps_val.yaml
config_override : []
gpu_ids : [0]
cpu_workers : 0
in_memory : False
checkpoint_path : checkpoints/updown.pth
output_path : /results/predictions.json
evalai_submit : False
Traceback (most recent call last):
File "scripts/inference.py", line 117, in
model.load_state_dict(torch.load(_A.checkpoint_path)["model"])
File "/root/anaconda3/envs/updown/lib/python3.6/site-packages/torch/nn/modules/module.py", line 777, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for UpDownCaptioner:
size mismatch for _embedding_layer.weight: copying a param with shape torch.Size([10315, 1000]) from checkpoint, the shape in current model is torch.Size([10306, 1000]).
size mismatch for _output_layer.weight: copying a param with shape torch.Size([10315, 1200]) from checkpoint, the shape in current model is torch.Size([10306, 1200]).
size mismatch for _output_layer.bias: copying a param with shape torch.Size([10315]) from checkpoint, the shape in current model is torch.Size([10306]).
The text was updated successfully, but these errors were encountered: