# Copyright (c) 2017 VisualDL Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ======================================================================= from __future__ import absolute_import import sys import time import numpy as np from visualdl.server.log import logger from visualdl.utils.string_util import encode_tag, decode_tag def get_components(log_reader): components = log_reader.components(update=True) components.add('graph') return list(components) def get_runs(log_reader): return log_reader.runs() def get_tags(log_reader): return log_reader.tags() def get_logs(log_reader, component): all_tag = log_reader.data_manager.get_reservoir(component).keys tags = {} for item in all_tag: index = item.rfind('/') run = item[0:index] tag = encode_tag(item[index + 1:]) if run in tags.keys(): tags[run].append(tag) else: tags[run] = [tag] return tags def get_scalar_tags(log_reader): return get_logs(log_reader, "scalar") def get_scalar(log_reader, run, tag): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("scalar").get_items( run, decode_tag(tag)) results = [[item.timestamp, item.id, item.value] for item in records] return results def get_image_tags(log_reader): return get_logs(log_reader, "image") def get_image_tag_steps(log_reader, run, tag): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("image").get_items( run, decode_tag(tag)) result = [{ "step": item.id, "wallTime": item.timestamp } for item in records] return result def get_individual_image(log_reader, run, tag, step_index): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("image").get_items( run, decode_tag(tag)) return records[step_index].image.encoded_image_string def get_audio_tags(log_reader): return get_logs(log_reader, "audio") def get_audio_tag_steps(log_reader, run, tag): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("audio").get_items( run, decode_tag(tag)) result = [{ "step": item.id, "wallTime": item.timestamp } for item in records] return result def get_individual_audio(log_reader, run, tag, step_index): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("audio").get_items( run, decode_tag(tag)) result = records[step_index].audio.encoded_image_string return result def get_embeddings_tags(log_reader): return get_logs(log_reader, "embeddings") def get_histogram_tags(log_reader): return get_logs(log_reader, "histogram") def get_embeddings(log_reader, run, tag, reduction, dimension=2): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("embeddings").get_items( run, decode_tag(tag)) labels = [] vectors = [] for item in records[0].embeddings.embeddings: labels.append(item.label) vectors.append(item.vectors) vectors = np.array(vectors) if reduction == 'tsne': import visualdl.server.tsne as tsne low_dim_embs = tsne.tsne( vectors, dimension, initial_dims=50, perplexity=30.0) elif reduction == 'pca': low_dim_embs = simple_pca(vectors, dimension) return {"embedding": low_dim_embs.tolist(), "labels": labels} def get_histogram(log_reader, run, tag): log_reader.load_new_data() records = log_reader.data_manager.get_reservoir("histogram").get_items( run, decode_tag(tag)) results = [] for item in records: histogram = item.histogram hist = histogram.hist bin_edges = histogram.bin_edges histogram_data = [] for index in range(len(hist)): histogram_data.append([bin_edges[index], bin_edges[index+1], hist[index]]) results.append([item.timestamp, item.id, histogram_data]) return results def retry(ntimes, function, time2sleep, *args, **kwargs): ''' try to execute `function` `ntimes`, if exception catched, the thread will sleep `time2sleep` seconds. ''' for i in range(ntimes): try: return function(*args, **kwargs) except Exception: error_info = '\n'.join(map(str, sys.exc_info())) logger.error("Unexpected error: %s" % error_info) time.sleep(time2sleep) def cache_get(cache): def _handler(key, func, *args, **kwargs): data = cache.get(key) if data is None: logger.warning('update cache %s' % key) data = func(*args, **kwargs) cache.set(key, data) return data return data return _handler def simple_pca(x, dimension): """ A simple PCA implementation to do the dimension reduction. """ # Center the data. x -= np.mean(x, axis=0) # Computing the Covariance Matrix cov = np.cov(x, rowvar=False) # Get eigenvectors and eigenvalues from the covariance matrix eigvals, eigvecs = np.linalg.eig(cov) # Sort the eigvals from high to low order = np.argsort(eigvals)[::-1] # Drop the eigenvectors with low eigenvalues eigvecs = eigvecs[:, order[:dimension]] return np.dot(x, eigvecs)