./data/seginw/Airplane-Parts is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: Airplane . Body . Cockpit . Engine . Wing . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.03s). Accumulating evaluation results... DONE (t=0.03s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.323 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.389 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.480 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.531 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.384 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.445 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.581 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.729 Final results: [0.3756864279270625, 0.5308008595890644, 0.37882019756215113, 0.383993399339934, 0.3939833084795228, 0.6256601374423156, 0.4446666666666667, 0.581, 0.581, 0.5, 0.5683333333333332, 0.7285714285714288] ./data/seginw/Bottles is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: bottle . can . label . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.673 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.742 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.696 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.529 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.854 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.860 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.860 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.663 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.741 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.686 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.663 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.532 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.843 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.854 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854 Final results: [0.6626787513291967, 0.7410354550908884, 0.6860321238854317, -1.0, -1.0, 0.6629373463152295, 0.5324625566004877, 0.8431905259491467, 0.8538488331591781, -1.0, -1.0, 0.8538488331591781] ./data/seginw/Brain-Tumor is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: tumor . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.195 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.310 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.633 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.781 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.149 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.098 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.643 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 Final results: [0.12001396443868577, 0.19069205611404016, 0.1487699392293854, -1.0, 0.3123744228271123, 0.09785765198870178, 0.0, 0.5216216216216216, 0.6432432432432431, -1.0, 0.6, 0.7] ./data/seginw/Chicken is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: chicken . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.00s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.753 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.825 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.771 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.753 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.036 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.340 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.836 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.820 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.840 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.845 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.930 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.853 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.841 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.040 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900 Final results: [0.8445395907890446, 0.9302970297029702, 0.9302970297029702, -1.0, 0.8527581329561527, 0.8412297096582723, 0.039999999999999994, 0.36, 0.9, -1.0, 0.9, 0.9] ./data/seginw/Cows is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: cow . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/60 images. time: 19.67s, ETA: 21.02s processed 59/60 images. time: 38.95s, ETA: 0.66s Accumulating evaluation results... DONE (t=0.04s). Accumulating evaluation results... DONE (t=0.03s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.586 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.810 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.700 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.164 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.726 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.803 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.792 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.804 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.560 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.799 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.658 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.830 Final results: [0.47811492252895554, 0.8036358041935029, 0.560070607670227, -1.0, 0.4698286333720617, 0.7992425760573034, 0.14638783269961977, 0.612167300380228, 0.6577946768060836, -1.0, 0.638135593220339, 0.8296296296296296] ./data/seginw/Electric-Shaver is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: caorau . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.832 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.860 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.856 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.832 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.817 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.917 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.933 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.933 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.721 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.860 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.856 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.721 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.725 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.808 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.829 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829 Final results: [0.7211879898777362, 0.8601922038699978, 0.855851447213687, -1.0, -1.0, 0.7211984956177734, 0.725, 0.8083333333333332, 0.8291666666666668, -1.0, -1.0, 0.8291666666666668] ./data/seginw/Elephants is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: elephant . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/99 images. time: 19.36s, ETA: 46.74s processed 59/99 images. time: 38.31s, ETA: 25.98s processed 89/99 images. time: 57.33s, ETA: 6.44s Accumulating evaluation results... DONE (t=0.07s). Accumulating evaluation results... DONE (t=0.06s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.802 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.407 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.862 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.479 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.867 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.913 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.750 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.889 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.934 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.775 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.925 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.618 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.842 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.465 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.831 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.869 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.750 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.837 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.892 Final results: [0.7750377694498445, 0.9254450462620254, 0.8776366940611678, 0.3248982041061249, 0.6175165126446855, 0.8416365886776701, 0.4647668393782383, 0.8310880829015543, 0.8694300518134714, 0.75, 0.8365079365079365, 0.8919354838709678] ./data/seginw/Fruits is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: apple . lemon . orange . pear . strawberry . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.03s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.817 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.881 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.881 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.801 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.853 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.883 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.883 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.879 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.823 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.881 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.881 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.840 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.877 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.877 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.877 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.871 Final results: [0.8229372937293729, 0.8811881188118812, 0.8811881188118812, -1.0, 0.9, 0.8395214521452145, 0.8766666666666666, 0.8766666666666666, 0.8766666666666666, -1.0, 0.9, 0.8708333333333332] ./data/seginw/Garbage is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: bin . garbage . pavement . road . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/142 images. time: 19.33s, ETA: 75.33s processed 59/142 images. time: 38.41s, ETA: 54.03s processed 89/142 images. time: 57.44s, ETA: 34.20s processed 119/142 images. time: 76.41s, ETA: 14.77s Accumulating evaluation results... DONE (t=0.12s). Accumulating evaluation results... DONE (t=0.11s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.381 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.343 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.800 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.016 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.838 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.870 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.800 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.904 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.336 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.011 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.284 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.744 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.775 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.804 Final results: [0.2500298897198872, 0.3362147086694614, 0.260681702612343, 0.6999999999999998, 0.011260271495086701, 0.2835845960750263, 0.47189496444885853, 0.7435502120829259, 0.7752321310413673, 0.7, 0.36583333333333334, 0.8042297316653935] ./data/seginw/Ginger-Garlic is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: garlic . ginger . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.500 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.587 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.506 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.198 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.830 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.864 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.860 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.833 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.820 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.837 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.832 Final results: [0.4564294032344411, 0.5362198866945519, 0.5362198866945519, -1.0, 0.8333333333333331, 0.6137990674067407, 0.15227272727272728, 0.8204545454545455, 0.8371212121212123, -1.0, 0.9, 0.8316666666666667] ./data/seginw/Hand is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: Hand-Segmentation . hand . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/60 images. time: 19.06s, ETA: 20.38s processed 59/60 images. time: 38.00s, ETA: 0.64s Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.727 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.964 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.672 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.978 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.978 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.978 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.748 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.908 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.712 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.749 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.778 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.965 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.967 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.967 Final results: [0.7482272742844522, 0.9083143257182247, 0.7118459449517528, -1.0, -1.0, 0.7485556061856102, 0.7783333333333334, 0.9650000000000001, 0.9666666666666666, -1.0, -1.0, 0.9666666666666666] ./data/seginw/Hand-Metal is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: hand . metal . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/65 images. time: 20.75s, ETA: 25.76s processed 59/65 images. time: 41.31s, ETA: 4.20s Accumulating evaluation results... DONE (t=0.05s). Accumulating evaluation results... DONE (t=0.04s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.809 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.907 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.842 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.638 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.916 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.936 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.925 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.936 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.812 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.903 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.839 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.841 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.650 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.899 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.920 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.950 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.917 Final results: [0.811581650403562, 0.9030538252178439, 0.8385466522615975, -1.0, 0.4008052668329053, 0.8407598492563809, 0.6503327417923691, 0.8985137533274179, 0.9201197870452527, -1.0, 0.95, 0.9168439716312058] ./data/seginw/House-Parts is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.01s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: aluminium door . aluminium window . cellar window . mint cond roof . plaster . plastic door . plastic window . plate fascade . wooden door . wooden fascade . wooden window . worn cond roof . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/201 images. time: 19.37s, ETA: 114.89s processed 59/201 images. time: 38.42s, ETA: 92.48s processed 89/201 images. time: 57.74s, ETA: 72.67s processed 119/201 images. time: 76.84s, ETA: 52.95s processed 149/201 images. time: 96.10s, ETA: 33.54s processed 179/201 images. time: 115.44s, ETA: 14.19s Accumulating evaluation results... DONE (t=0.32s). Accumulating evaluation results... DONE (t=0.31s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.100 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.146 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.109 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.202 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.424 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.261 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.085 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.131 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.091 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.382 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.402 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.536 Final results: [0.08521419957390203, 0.1305643715731481, 0.09122843553279428, 0.03481824173099917, 0.09464966751895551, 0.24305259622829994, 0.215129607795673, 0.3822629848065605, 0.4016862913674572, 0.2914742653369344, 0.45406752713313425, 0.5360874195595035] ./data/seginw/HouseHold-Items is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: bottle . mouse . perfume . phone . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.601 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.626 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.601 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.626 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700 Final results: [0.600990099009901, 0.6262376237623762, 0.6262376237623762, -1.0, -1.0, 0.600990099009901, 0.7, 0.7, 0.7, -1.0, -1.0, 0.7] ./data/seginw/Nutterfly-Squireel is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.01s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: butterfly . squirrel . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/237 images. time: 19.57s, ETA: 140.38s processed 59/237 images. time: 38.56s, ETA: 116.33s processed 89/237 images. time: 57.58s, ETA: 95.75s processed 119/237 images. time: 76.67s, ETA: 76.02s processed 149/237 images. time: 96.05s, ETA: 56.73s processed 179/237 images. time: 115.22s, ETA: 37.33s processed 209/237 images. time: 134.30s, ETA: 17.99s Accumulating evaluation results... DONE (t=0.14s). Accumulating evaluation results... DONE (t=0.14s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.811 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.981 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.890 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.571 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.817 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.808 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.903 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.914 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.918 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.771 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.966 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.837 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.783 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.765 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.838 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.854 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.861 Final results: [0.7712714511977438, 0.9663068379224936, 0.8373429687766779, 0.3673267326732673, 0.8, 0.7827147283752893, 0.7645104895104896, 0.8377972027972026, 0.8537062937062938, 0.36666666666666664, 0.8, 0.8609621765096218] ./data/seginw/Phones is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: phone . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.380 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.570 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.850 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.151 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.638 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.815 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.713 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.841 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.850 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.466 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.249 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.647 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.590 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.764 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.662 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.793 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750 Final results: [0.35250107505272243, 0.466454498387027, 0.46259573960473505, 0.24907264112924304, 0.6474120646522649, 0.7504950495049505, 0.14102564102564102, 0.5897435897435898, 0.764102564102564, 0.6625, 0.793103448275862, 0.75] ./data/seginw/Poles is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: poles . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.667 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.667 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.667 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.667 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.201 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.442 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.112 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.367 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.400 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.400 Final results: [0.2006698581946107, 0.4422442244224422, 0.1122112211221122, -1.0, -1.0, 0.3735973597359736, 0.3333333333333333, 0.36666666666666664, 0.4, -1.0, -1.0, 0.4] ./data/seginw/Puppies is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: puppy . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.01s). Accumulating evaluation results... DONE (t=0.00s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.533 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.333 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.817 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.817 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.817 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.547 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.767 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.767 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.767 Final results: [0.5011138613861387, 0.5474422442244224, 0.5474422442244224, -1.0, -1.0, 0.5430693069306931, 0.31666666666666665, 0.7666666666666667, 0.7666666666666667, -1.0, -1.0, 0.7666666666666667] ./data/seginw/Rail is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.01s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: rail . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/1069 images. time: 34.60s, ETA: 1240.89s processed 59/1069 images. time: 68.43s, ETA: 1171.51s processed 89/1069 images. time: 102.29s, ETA: 1126.35s processed 119/1069 images. time: 136.16s, ETA: 1086.99s processed 149/1069 images. time: 170.20s, ETA: 1050.92s processed 179/1069 images. time: 204.09s, ETA: 1014.76s processed 209/1069 images. time: 238.03s, ETA: 979.44s processed 239/1069 images. time: 271.93s, ETA: 944.37s processed 269/1069 images. time: 306.00s, ETA: 910.03s processed 299/1069 images. time: 340.04s, ETA: 875.68s processed 329/1069 images. time: 374.03s, ETA: 841.29s processed 359/1069 images. time: 408.02s, ETA: 806.96s processed 389/1069 images. time: 442.02s, ETA: 772.69s processed 419/1069 images. time: 476.07s, ETA: 738.54s processed 449/1069 images. time: 510.08s, ETA: 704.34s processed 479/1069 images. time: 544.07s, ETA: 670.15s processed 509/1069 images. time: 578.01s, ETA: 635.92s processed 539/1069 images. time: 612.02s, ETA: 601.80s processed 569/1069 images. time: 646.00s, ETA: 567.66s processed 599/1069 images. time: 680.00s, ETA: 533.55s processed 629/1069 images. time: 714.08s, ETA: 499.51s processed 659/1069 images. time: 748.05s, ETA: 465.40s processed 689/1069 images. time: 782.04s, ETA: 431.31s processed 719/1069 images. time: 816.01s, ETA: 397.22s processed 749/1069 images. time: 850.23s, ETA: 363.25s processed 779/1069 images. time: 884.24s, ETA: 329.18s processed 809/1069 images. time: 918.22s, ETA: 295.10s processed 839/1069 images. time: 952.23s, ETA: 261.04s processed 869/1069 images. time: 986.31s, ETA: 227.00s processed 899/1069 images. time: 1020.36s, ETA: 192.95s processed 929/1069 images. time: 1054.35s, ETA: 158.89s processed 959/1069 images. time: 1088.34s, ETA: 124.84s processed 989/1069 images. time: 1122.40s, ETA: 90.79s processed 1019/1069 images. time: 1156.39s, ETA: 56.74s processed 1049/1069 images. time: 1190.53s, ETA: 22.70s Accumulating evaluation results... DONE (t=0.44s). Accumulating evaluation results... DONE (t=0.46s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.280 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.386 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.283 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.782 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.823 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.823 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.077 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.149 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.076 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.078 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.130 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515 Final results: [0.0769556193399893, 0.14927162129175225, 0.0761471744937495, -1.0, -1.0, 0.07815575228838245, 0.1300469483568075, 0.37361502347417835, 0.5149295774647886, -1.0, -1.0, 0.5149295774647886] ./data/seginw/Salmon-Fillet is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: Salmon_fillet . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/64 images. time: 17.93s, ETA: 21.63s processed 59/64 images. time: 35.50s, ETA: 3.01s Accumulating evaluation results... DONE (t=0.05s). Accumulating evaluation results... DONE (t=0.04s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.525 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.505 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.230 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.274 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.704 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.505 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.830 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.898 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.777 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.872 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.963 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.422 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.521 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.475 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.699 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.443 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.775 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.886 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.869 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.872 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900 Final results: [0.4219736348966027, 0.5212969201708311, 0.47514031785388383, 0.1998890197601088, 0.14054668695467623, 0.6994578064581743, 0.4428571428571428, 0.7746031746031746, 0.8857142857142858, 0.8692307692307694, 0.8722222222222221, 0.9] ./data/seginw/Strawberry is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: R_strawberry . people . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/87 images. time: 19.08s, ETA: 38.16s processed 59/87 images. time: 37.80s, ETA: 17.94s Accumulating evaluation results... DONE (t=0.05s). Accumulating evaluation results... DONE (t=0.04s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.748 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.987 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.722 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.770 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.869 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.941 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.875 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.942 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.856 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.993 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.979 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.876 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.799 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.902 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.915 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.746 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.933 Final results: [0.8560205476363458, 0.9934628003104631, 0.9788546395632423, -1.0, 0.6056996457824525, 0.8758954855483145, 0.7985714285714286, 0.9023809523809524, 0.9152380952380954, -1.0, 0.7458333333333333, 0.9327160493827161] ./data/seginw/Tablets is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: tablets . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.01s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.406 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.372 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.184 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.623 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.619 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.795 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.718 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.802 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.967 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.297 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.395 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.376 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.108 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.511 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.305 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.592 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.760 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.664 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.776 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.867 Final results: [0.2974654734783489, 0.39498449143433517, 0.3760285149568145, 0.10840134508258337, 0.5114763400755867, 0.3045544554455446, 0.02857142857142857, 0.5920634920634921, 0.7603174603174603, 0.6636363636363636, 0.7755102040816327, 0.8666666666666666] ./data/seginw/Toolkits is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: Allen-key . block . gasket . plier . prism . screw . screwdriver . wrench . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.03s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.358 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.686 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.686 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.623 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 1.000 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.218 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.159 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.875 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.317 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.594 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.594 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.533 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.875 Final results: [0.21828670839691072, 0.26240076627506675, 0.25119012140105557, -1.0, 0.15940564275357214, 0.8752475247524752, 0.31666666666666665, 0.5944444444444446, 0.5944444444444446, -1.0, 0.5333333333333333, 0.875] ./data/seginw/Trash is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: Aluminium foil . Cigarette . Clear plastic bottle . Corrugated carton . Disposable plastic cup . Drink Can . Egg Carton . Foam cup . Food Can . Garbage bag . Glass bottle . Glass cup . Metal bottle cap . Other carton . Other plastic bottle . Paper cup . Plastic bag - wrapper . Plastic bottle cap . Plastic lid . Plastic straw . Pop tab . Styrofoam piece . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] processed 29/92 images. time: 21.08s, ETA: 45.80s processed 59/92 images. time: 41.82s, ETA: 23.39s processed 89/92 images. time: 62.48s, ETA: 2.11s Accumulating evaluation results... DONE (t=0.20s). Accumulating evaluation results... DONE (t=0.20s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.275 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.301 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.053 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.241 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.478 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.489 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.684 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.701 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.916 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.307 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.037 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.252 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.530 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.716 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.732 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.648 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.917 Final results: [0.2996620218030882, 0.3280667014726521, 0.3074068449567568, 0.03688755640411023, 0.25170232203926396, 0.5421461589287967, 0.5298507537047453, 0.7163501103005305, 0.7323701569205769, 0.18611771363893603, 0.6476133241758242, 0.9167420814479639] ./data/seginw/Watermelon is data path ! /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). final text_encoder_type: bert-base-uncased loading annotations into memory... Done (t=0.00s) creating index... index created! final text_encoder_type: bert-base-uncased Input text prompt: watermelon . huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) /scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. warnings.warn( test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). topk_boxes = topk_indexes // prob.shape[2] Accumulating evaluation results... DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.02s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.680 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.846 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.798 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.627 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.508 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.649 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.841 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.770 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.914 IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.656 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.848 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.722 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.607 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.743 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.129 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.642 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.821 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.820 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.885 Final results: [0.6560782166097321, 0.8475986453221692, 0.7219562187024128, 0.6071831806626943, 0.46873353497361103, 0.7434856932206481, 0.12857142857142856, 0.6419642857142857, 0.8205357142857144, 0.6741935483870968, 0.82, 0.8845070422535211]