[example] add stable diffuser (#1825)

examples/images/diffusion/scripts/train_searcher.py

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+import os, sys
+import numpy as np
+import scann
+import argparse
+import glob
+from multiprocessing import cpu_count
+from tqdm import tqdm
+
+from ldm.util import parallel_data_prefetch
+
+
+def search_bruteforce(searcher):
+    return searcher.score_brute_force().build()
+
+
+def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
+                        partioning_trainsize, num_leaves, num_leaves_to_search):
+    return searcher.tree(num_leaves=num_leaves,
+                         num_leaves_to_search=num_leaves_to_search,
+                         training_sample_size=partioning_trainsize). \
+        score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
+
+
+def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
+    return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
+        reorder_k).build()
+
+def load_datapool(dpath):
+
+
+    def load_single_file(saved_embeddings):
+        compressed = np.load(saved_embeddings)
+        database = {key: compressed[key] for key in compressed.files}
+        return database
+
+    def load_multi_files(data_archive):
+        database = {key: [] for key in data_archive[0].files}
+        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+            for key in d.files:
+                database[key].append(d[key])
+
+        return database
+
+    print(f'Load saved patch embedding from "{dpath}"')
+    file_content = glob.glob(os.path.join(dpath, '*.npz'))
+
+    if len(file_content) == 1:
+        data_pool = load_single_file(file_content[0])
+    elif len(file_content) > 1:
+        data = [np.load(f) for f in file_content]
+        prefetched_data = parallel_data_prefetch(load_multi_files, data,
+                                                 n_proc=min(len(data), cpu_count()), target_data_type='dict')
+
+        data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
+    else:
+        raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
+
+    print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
+    return data_pool
+
+
+def train_searcher(opt,
+                   metric='dot_product',
+                   partioning_trainsize=None,
+                   reorder_k=None,
+                   # todo tune
+                   aiq_thld=0.2,
+                   dims_per_block=2,
+                   num_leaves=None,
+                   num_leaves_to_search=None,):
+
+    data_pool = load_datapool(opt.database)
+    k = opt.knn
+
+    if not reorder_k:
+        reorder_k = 2 * k
+
+    # normalize
+    # embeddings =
+    searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
+    pool_size = data_pool['embedding'].shape[0]
+
+    print(*(['#'] * 100))
+    print('Initializing scaNN searcher with the following values:')
+    print(f'k: {k}')
+    print(f'metric: {metric}')
+    print(f'reorder_k: {reorder_k}')
+    print(f'anisotropic_quantization_threshold: {aiq_thld}')
+    print(f'dims_per_block: {dims_per_block}')
+    print(*(['#'] * 100))
+    print('Start training searcher....')
+    print(f'N samples in pool is {pool_size}')
+
+    # this reflects the recommended design choices proposed at
+    # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
+    if pool_size < 2e4:
+        print('Using brute force search.')
+        searcher = search_bruteforce(searcher)
+    elif 2e4 <= pool_size and pool_size < 1e5:
+        print('Using asymmetric hashing search and reordering.')
+        searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+    else:
+        print('Using using partioning, asymmetric hashing search and reordering.')
+
+        if not partioning_trainsize:
+            partioning_trainsize = data_pool['embedding'].shape[0] // 10
+        if not num_leaves:
+            num_leaves = int(np.sqrt(pool_size))
+
+        if not num_leaves_to_search:
+            num_leaves_to_search = max(num_leaves // 20, 1)
+
+        print('Partitioning params:')
+        print(f'num_leaves: {num_leaves}')
+        print(f'num_leaves_to_search: {num_leaves_to_search}')
+        # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+        searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
+                                                 partioning_trainsize, num_leaves, num_leaves_to_search)
+
+    print('Finish training searcher')
+    searcher_savedir = opt.target_path
+    os.makedirs(searcher_savedir, exist_ok=True)
+    searcher.serialize(searcher_savedir)
+    print(f'Saved trained searcher under "{searcher_savedir}"')
+
+if __name__ == '__main__':
+    sys.path.append(os.getcwd())
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--database',
+                        '-d',
+                        default='data/rdm/retrieval_databases/openimages',
+                        type=str,
+                        help='path to folder containing the clip feature of the database')
+    parser.add_argument('--target_path',
+                        '-t',
+                        default='data/rdm/searchers/openimages',
+                        type=str,
+                        help='path to the target folder where the searcher shall be stored.')
+    parser.add_argument('--knn',
+                        '-k',
+                        default=20,
+                        type=int,
+                        help='number of nearest neighbors, for which the searcher shall be optimized')
+
+    opt, _  = parser.parse_known_args()
+
+    train_searcher(opt,)