shape error when generating heatmap using template

[kaczmarj]

hi I am trying to generate the sample heatmap and I get a shape error. here is the command:

CUDA_VISIBLE_DEVICES=1 python create_heatmaps.py --config config_template.yaml

and here is the traceback

Traceback (most recent call last):
  File "/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/create_heatmaps.py", line 371, in <module>
    compute_from_patches(wsi_object=wsi_object, clam_pred=Y_hats[0], model=model, feature_extractor=feature_extractor, batch_size=exp_args.batch_size, **wsi_kwargs, 
  File "/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/vis_utils/heatmap_utils.py", line 77, in compute_from_patches
    A[score_idx] = score2percentile(A[score_idx], ref_scores)
ValueError: could not broadcast input array from shape (195,) into shape (1,)

I did not change the create_heatmaps.py script. how can i solve this issue?

All console output from the command:

exp_arguments
n_classes : 2
save_exp_code : HEATMAP_OUTPUT
raw_save_dir : heatmaps/heatmap_raw_results
production_save_dir : heatmaps/heatmap_production_results
batch_size : 384

data_arguments
data_dir : heatmaps/demo/slides/
data_dir_key : source
process_list : heatmap_demo_dataset.csv
preset : presets/bwh_biopsy.csv
slide_ext : .svs
label_dict : {'LUAD': 0, 'LSCC': 1}

patching_arguments
patch_size : 256
overlap : 0.5
patch_level : 0
custom_downsample : 1

model_arguments
ckpt_path : heatmaps/demo/ckpts/s_0_checkpoint.pt
model_type : clam_sb
initiate_fn : initiate_model
model_size : small
drop_out : True

heatmap_arguments
vis_level : 1
alpha : 0.4
blank_canvas : False
save_orig : True
save_ext : jpg
use_ref_scores : True
blur : False
use_center_shift : True
use_roi : False
calc_heatmap : True
binarize : False
binary_thresh : -1
custom_downsample : 1
cmap : jet

sample_arguments
samples : [{'name': 'topk_high_attention', 'sample': True, 'seed': 1, 'k': 15, 'mode': 'topk'}]
Continue? Y/N y
patch_size: 256 x 256, with 0.50 overlap, step size is 128 x 128

list of slides to process: 
       slide_id label  process status  seg_level  sthresh  ...  a_h  max_n_holes  vis_level line_thickness use_padding  contour_fn
0  C3L-03262-22  LUAD        1    tbp         -1       15  ...  1.0            2         -1             50        True     four_pt
1  C3L-01663-21  LSCC        1    tbp         -1       15  ...  1.0            2         -1             50        True     four_pt

[2 rows x 18 columns]

initializing model from checkpoint

ckpt path: heatmaps/demo/ckpts/s_0_checkpoint.pt
Init Model
CLAM_SB(
  (attention_net): Sequential(
    (0): Linear(in_features=1024, out_features=512, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.25, inplace=False)
    (3): Attn_Net_Gated(
      (attention_a): Sequential(
        (0): Linear(in_features=512, out_features=256, bias=True)
        (1): Tanh()
        (2): Dropout(p=0.25, inplace=False)
      )
      (attention_b): Sequential(
        (0): Linear(in_features=512, out_features=256, bias=True)
        (1): Sigmoid()
        (2): Dropout(p=0.25, inplace=False)
      )
      (attention_c): Linear(in_features=256, out_features=1, bias=True)
    )
  )
  (classifiers): Linear(in_features=512, out_features=2, bias=True)
  (instance_classifiers): ModuleList(
    (0): Linear(in_features=512, out_features=2, bias=True)
    (1): Linear(in_features=512, out_features=2, bias=True)
  )
  (instance_loss_fn): CrossEntropyLoss()
)
Total number of parameters: 790791
Total number of trainable parameters: 790791
Done!

processing:  C3L-03262-22.svs
slide id:  C3L-03262-22
top left:  None  bot right:  None
seg_level: -1
sthresh: 15
mthresh: 11
close: 2
use_otsu: False
keep_ids: []
exclude_ids: []
a_t: 1
a_h: 1
max_n_holes: 2
vis_level: -1
line_thickness: 50
Initializing WSI object
Done!
Y_hat: LUAD, Y: LUAD, Y_prob: ['0.9962', '0.0038']
sampling topk_high_attention
coord: [5732 3396] score: 100.000
coord: [3172 3652] score: 99.487
coord: [4964 5444] score: 98.974
coord: [4964 3908] score: 98.462
coord: [3684 3396] score: 97.949
coord: [3172 3908] score: 97.436
coord: [5988 4932] score: 96.923
coord: [5988 4676] score: 96.410
coord: [5988 3396] score: 95.897
coord: [4964 5188] score: 95.385
coord: [6244 4676] score: 94.872
coord: [3172 4420] score: 94.359
coord: [5476 3396] score: 93.846
coord: [5732 4164] score: 93.333
coord: [4196 4164] score: 92.821

creating heatmap for: 
top_left:  (0, 0) bot_right:  (9959, 9023)
w: 2489, h: 2255
scaled patch size:  [64 64]

computing foreground tissue mask
detected 899730/5612695 of region as tissue

computing heatmap image
total of 195 patches
progress: 38/195
progress: 77/195
progress: 116/195
progress: 155/195
progress: 194/195
Done

computing blend
using block size: 1024 x 1024
processing 0/2 contours
Bounding Box: 5681 6038 1842 1077
Contour Area: 628940.0
Extracted 13 coordinates
processing 1/2 contours
Bounding Box: 2404 2884 4847 3951
Contour Area: 13722139.0
Extracted 760 coordinates
filtered a total of 773 coordinates
total number of patches to process:  773
number of batches:  3
/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/utils/utils.py:37: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484808560/work/torch/csrc/utils/tensor_new.cpp:201.)
  label = torch.LongTensor([item[1] for item in batch])
/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/utils/utils.py:37: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484808560/work/torch/csrc/utils/tensor_new.cpp:201.)
  label = torch.LongTensor([item[1] for item in batch])
/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/utils/utils.py:37: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484808560/work/torch/csrc/utils/tensor_new.cpp:201.)
  label = torch.LongTensor([item[1] for item in batch])
Traceback (most recent call last):
  File "/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/create_heatmaps.py", line 371, in <module>
    compute_from_patches(wsi_object=wsi_object, clam_pred=Y_hats[0], model=model, feature_extractor=feature_extractor, batch_size=exp_args.batch_size, **wsi_kwargs, 
  File "/mnt/data08/shared/jkaczmarzyk/mtl/CLAM/vis_utils/heatmap_utils.py", line 77, in compute_from_patches
    A[score_idx] = score2percentile(A[score_idx], ref_scores)
ValueError: could not broadcast input array from shape (195,) into shape (1,)

[kaczmarj]

i got it working, but i do not know if my solution is valid. i squeezed the dimensions of both arrays in score2percentile

def score2percentile(score, ref):
    percentile = percentileofscore(ref.squeeze(), score.squeeze())
    return percentile