backup

calculate_error.py

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+import os
+import sys
+from configs.config_abl_abs_skirt import config
+
+os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
+# import tensorflow as tf
+
+# gpus = tf.config.experimental.list_physical_devices('GPU')
+# if gpus:
+#     try:
+#         # Currently, memory growth needs to be the same across GPUs
+#         for gpu in gpus:
+#             tf.config.experimental.set_memory_growth(gpu, True)
+#         logical_gpus = tf.config.experimental.list_logical_devices('GPU')
+#         print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+#     except RuntimeError as e:
+#         # Memory growth must be set before GPUs have been initialized
+#         print(e)
+
+import csv
+import glob
+import time
+import argparse
+import wandb
+import tqdm
+import random
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as ex
+
+# if config['is_skirt']:
+#     from losses import edge_loss_skirt as edge_loss
+# else:
+#     from losses import edge_loss
+
+
+
+# from postprocessing import fix_collision
+# from losses import collision_ratio, kl_loss #, concept_loss
+# # from losses import collision_penalty as collision_penalty
+# from losses import collision_loss as collision_penalty
+# from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
+# from custom_utils.smpl import SMPL
+# from custom_utils.supersmpl_io import writeOBJ
+
+
+
+
+# uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('--name', type=str)
+    args = vars(parser.parse_args())
+
+    # smpl = SMPL(use_one_hot=config['use_one_hot'])
+    skirt_modifier = '_skirt' if config['is_skirt'] else ''
+
+    folder = config['test_dir']
+
+    checkpoint_name = config['name']
+    iteration = '70000'
+
+    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()
+
+
+    output_folder = '/home/workspace/data/results/' + checkpoint_name + '-' + iteration
+
+
+    faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
+    ids = os.listdir(faces_folder)
+    ids = [id.split('.')[0] for id in ids]
+    faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
+
+    masks_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready/mesh_mask'
+    ids = os.listdir(masks_folder)
+    ids = [id.split('.')[0] for id in ids]
+    masks = {id : np.load(masks_folder + '/' + id + '.npy') for id in ids }
+
+    frames = os.listdir(output_folder)
+    frames = [frame for frame in frames if not frame.endswith('_fixed_coll.npy')]
+
+    if config['remove_global_orientation']:
+        gt_name = 'mesh_posed_no_orient'
+    else:
+        gt_name = 'mesh_posed'
+
+    orig_folder = '/home/workspace/data/dataset/test' + ('_skirt' if config['is_skirt'] else '') + '_ready' + ('_2' if config['is_skirt'] else '') + '/' + gt_name
+
+    errors = dict()
+    if config['is_skirt']:
+        per_garment_errors = { 'Tshirt': [], 'Dress': [], 'Skirt': [], 'Top': [] }
+    else:
+        per_garment_errors = { 'Tshirt': [], 'Jumpsuit': [], 'Trousers': [], 'Top': [] }
+
+    for frame in tqdm.tqdm(frames):
+        id = frame.split('_')[0]
+        frame_nr = frame.split('.')[0].split('_')[1]
+        gt = np.load(os.path.join(orig_folder, id + '_' + frame_nr + '.npz'))[gt_name][masks[id], :].astype(np.float32)
+        pred = np.load(os.path.join(output_folder, id + '_' + frame_nr + '.npy'))
+        metric = np.mean(np.sqrt(np.sum((gt - pred) ** 2, axis=-1)))
+        errors[id + '_' + frame_nr] = metric
+        per_garment_errors[garment_type_table[id]].append(metric)
+
+
+    for key in per_garment_errors.keys():
+        print(key + ': ' + str(np.mean(per_garment_errors[key])))
+
+    with open('/home/workspace/data/results/errors/' + checkpoint_name + '-' + iteration + '.csv', mode='w') as f:
+        file_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
+
+        for key in sorted(errors.keys()):
+            file_writer.writerow([key, errors[key]])
+
+        # x = gt
+        # y = pred
+        # fig = go.Figure(data=[
+        #         go.Mesh3d(
+        #             x=x[:,0],
+        #             y=x[:,1],
+        #             z=x[:,2],
+        #             i = faces[id][:,0],
+        #             j = faces[id][:,1],
+        #             k = faces[id][:,2],
+        #         ),
+        #         go.Mesh3d(
+        #             x=y[:,0],
+        #             y=y[:,1],
+        #             z=y[:,2],
+        #             i = faces[id][:,0],
+        #             j = faces[id][:,1],
+        #             k = faces[id][:,2],
+        #         ),
+        #     ])
+
+        # fig.update_layout(scene_aspectmode='manual',
+        #                 scene_aspectratio=dict(x=1, y=1, z=1))
+        # fig.update_layout(
+        #     scene = dict(
+        #         xaxis = dict(nticks=4, range=[-1,1],),
+        #                     yaxis = dict(nticks=4, range=[-1,1],),
+        #                     zaxis = dict(nticks=4, range=[-1,1],),)
+        #                     )
+
+        # fig.show()
+
+    #     print()
+
+    # print()
+