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app.py

@@ -33,8 +33,9 @@ HEIGHT, WIDTH = 500, 500
 # chest
 SEX_CAT_CHEST = ["female", "male"]  # 0,1
 RACE_CAT = ["white", "black", "asian"]  # 0,1,2
-FIND_CAT = ["no disease", "consolidation", "opacity"] # 0,1,2
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+# FIND_CAT = ["no disease", "consolidation", "opacity"] # 0,1,2
+FIND_CAT = ["no disease", "effusion", "pneumonia"] # 0,1,2
+DEVICE = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
 
 
 class Hparams:
@@ -72,10 +73,10 @@ def get_paths(dataset_id):
         vae_path = "./checkpoints/m_b_v_s/ukbb192_beta5_dgauss_b33/checkpoint.pt"
     elif "Chest" in dataset_id:
         data_path = "./data/mimic_subset"
-        pgm_path = "./checkpoints/a_r_s_f/sup_pgm_mimic/checkpoint.pt"
+        pgm_path = "./checkpoints/a_r_s_f/sup_pgm_mimic/60k_checkpoint.pt"
         vae_path = [
-            "./checkpoints/a_r_s_f/mimic_beta9_gelu_dgauss_1_lr3/checkpoint.pt",  # base vae
-            "./checkpoints/a_r_s_f/mimic_dscm_lr_1e5_lagrange_lr_1_damping_10/6500_checkpoint.pt",  # cf trained DSCM
+            "./checkpoints/a_r_s_f/mimic_beta9_gelu_dgauss_1_lr3/60k_checkpoint.pt",  # base vae
+            "./checkpoints/a_r_s_f/mimic_dscm_lr_1e5_lagrange_lr_1_damping_10/60k_checkpoint.pt",  # cf trained DSCM
         ]
     return data_path, vae_path, pgm_path
 
@@ -580,25 +581,25 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
             with gr.Row(): #.style(equal_height=True):
                 idx_chest = gr.Number(value=0, visible=False)
                 with gr.Column(scale=1, min_width=200):
-                    x_chest = gr.Image(label="Observation", interactive=False,).style(height=HEIGHT,css=".label { font-size: 80px; }")
+                    x_chest = gr.Image(label="Observation", interactive=False, height=HEIGHT) #.style(height=HEIGHT,css=".label { font-size: 80px; }")
                 with gr.Column(scale=1, min_width=200):
                     cf_x_chest = gr.Image(
-                        label="Counterfactual", interactive=False).style(height=HEIGHT, css=".label { font-size: 80px; }")
+                        label="Counterfactual", interactive=False, height=HEIGHT) #.style(height=HEIGHT, css=".label { font-size: 80px; }")
                 with gr.Column(scale=1, min_width=200):
                     cf_x_std_chest = gr.Image(
-                        label="Counterfactual Uncertainty", interactive=False).style(height=HEIGHT,css=".label { font-size: 80px; }")
+                        label="Counterfactual Uncertainty", interactive=False, height=HEIGHT) #.style(height=HEIGHT,css=".label { font-size: 80px; }")
                 with gr.Column(scale=1, min_width=200):
                     effect_chest = gr.Image(
-                        label="Direct Causal Effect", interactive=False).style(height=HEIGHT,css=".label { font-size: 80px; }")
+                        label="Direct Causal Effect", interactive=False, height=HEIGHT) #.style(height=HEIGHT,css=".label { font-size: 80px; }")
 
             with gr.Row():
                 with gr.Column(scale=2.55):
-                    gr.Markdown(
-                        "**Intervention**"
-                        + 20 * " "
-                        + "[arXiv paper](https://arxiv.org/abs/2306.15764)   |   [GitHub code](https://github.com/biomedia-mira/causal-gen)"
-                        + "  |   Hint: try 90% zoom"
-                    )
+                    # gr.Markdown(
+                    #     "**Intervention**"
+                    #     + 20 * " "
+                    #     + "[arXiv paper](https://arxiv.org/abs/2306.15764)   |   [GitHub code](https://github.com/biomedia-mira/causal-gen)"
+                    #     + "  |   Hint: try 90% zoom"
+                    # )
                     with gr.Row(equal_height=True): #.style(equal_height=True):
                         with gr.Column(min_width=200):
                             do_f_chest = gr.Checkbox(label="do(disease)", value=False)
@@ -626,7 +627,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
                 with gr.Column(scale=1):
                     # gr.Markdown("###  ")
                     causal_graph_chest = gr.Image(
-                        label="Causal Graph", interactive=False).style(height=345)
+                        label="Causal Graph", interactive=False,height=345) #.style(height=345)
 
     # # morphomnist
     # do = [do_t, do_i, do_y]

app_prep

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Apr 17 19:47:32 2024
+
+@author: yanwe
+"""
+
+import pandas as pd
+import shutil
+import os
+
+test = pd.read_csv('/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/MIMIC_CXR/60k/meta/test.csv', nrows=500)
+test['path_preproc'] = None
+for n in range(500):
+    test.loc[n,'path_preproc'] = f'{n}.jpg'
+
+
+source_file = '/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/MIMIC_CXR/60k/data'
+target_directory = '/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/cf_app/data/mimic_subset'
+
+for n in range(500):
+    file_name = os.path.join(source_file, test.loc[n,'dicom_id'] + '.jpg')
+    new_name = os.path.join(target_directory, test.loc[n,'path_preproc'])
+    shutil.copy(file_name, new_name)
+
+
+def label_to_name(data):
+    if data['disease_label'] == 0:
+        return "No Finding"
+    elif data['disease_label'] == 1:
+        return "Pleural Effusion"
+    elif data['disease_label'] == 2:
+        return "Pneumonia"
+    else:
+        return "No Finding"
+    
+def label_to_sex(data):
+    if data['sex_label'] == 0:
+        return "Female"
+    elif data['sex_label'] == 1:
+        return "Male"
+    else:
+        return "Female"
+    
+def label_to_race(data):
+    if data['race_label'] == 0:
+        return "White"
+    elif data['race_label'] == 1:
+        return "Black"
+    elif data['race_label'] == 2:
+        return "Asian"
+
+
+test['disease'] = test.apply(label_to_name, axis=1)
+test['sex'] = test.apply(label_to_sex, axis=1)
+test['race'] = test.apply(label_to_race, axis=1)
+
+
+test.to_csv('/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/cf_app/data/mimic_subset/mimic.sample.test.csv')
+
+
+
+
+
+

checkpoints/a_r_s_f/mimic_beta9_gelu_dgauss_1_lr3/60k_checkpoint.pt

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+size 130217239

checkpoints/a_r_s_f/mimic_dscm_lr_1e5_lagrange_lr_1_damping_10/60k_checkpoint.pt

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+size 219922875

checkpoints/a_r_s_f/sup_pgm_mimic/60k_checkpoint.pt

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