text stringlengths 1 93.6k |
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darkerLoss = torch.mean(darkerWeight * torch.pow(F.relu(rf2 - rf1 + tau), 2) )
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return eqLoss, darkerLoss
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# <FILESEP>
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#!/usr/bin/env python
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import os
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import sys
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import time
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import json
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import numpy as np
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import tensorflow as tf
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import inference_utils
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import input_utils
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from lsgn_data import LSGNData
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from lsgn_evaluator import LSGNEvaluator
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from srl_model import SRLModel
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import util
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if __name__ == "__main__":
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util.set_gpus()
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name = sys.argv[1]
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input_filename = sys.argv[2]
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output_filename = sys.argv[3]
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print "Running experiment: {}.".format(name)
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config = util.get_config("experiments.conf")[name]
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config["log_dir"] = util.mkdirs(os.path.join(config["log_root"], name))
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print "Loading data from: {}.".format(input_filename)
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config["eval_path"] = input_filename
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config["batch_size"] = -1
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config["max_tokens_per_batch"] = -1
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# Use dev lm, if provided.
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if config["lm_path"] and "lm_path_dev" in config and config["lm_path_dev"]:
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config["lm_path"] = config["lm_path_dev"]
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util.print_config(config)
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data = LSGNData(config)
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model = SRLModel(data, config)
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evaluator = LSGNEvaluator(config)
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# Load data and model.
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eval_data, eval_tensors, doc_level_eval_data = data.load_eval_data()
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variables_to_restore = []
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for var in tf.global_variables():
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#print var.name
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if "module/" not in var.name:
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variables_to_restore.append(var)
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saver = tf.train.Saver(variables_to_restore)
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log_dir = config["log_dir"]
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with tf.Session() as session:
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checkpoint_path = os.path.join(log_dir, "model.max.ckpt")
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tf.global_variables_initializer().run()
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saver.restore(session, checkpoint_path)
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with open(output_filename, "w") as f:
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#for example_num, (tensorized_example, example) in enumerate(model.eval_data):
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for i, doc_tensors in enumerate(eval_tensors):
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feed_dict = dict(zip(
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data.input_tensors,
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[input_utils.pad_batch_tensors(doc_tensors, tn) for tn in data.input_names + data.label_names]))
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predict_names = []
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for tn in data.predict_names:
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if tn in model.predictions:
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predict_names.append(tn)
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predict_tensors = [model.predictions[tn] for tn in predict_names] + [model.loss]
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predict_tensors = session.run(predict_tensors, feed_dict=feed_dict)
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predict_dict = dict(zip(predict_names + ["loss"], predict_tensors))
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doc_example = doc_level_eval_data[i]
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sentences = doc_example["sentences"]
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predictions = inference_utils.srl_decode(
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sentences, predict_dict, data.srl_labels_inv, config)
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doc_example["predicted_srl"] = []
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word_offset = 0
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for j, sentence in enumerate(sentences):
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for pred, args in predictions["srl"][j].iteritems():
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doc_example["predicted_srl"].extend([
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[int(pred + word_offset), int(a[0] + word_offset),
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int(a[1] + word_offset), a[2]] for a in args])
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word_offset += len(sentence)
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f.write(json.dumps(doc_example))
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f.write("\n")
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if (i + 1) % 10 == 0:
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print "Decoded {} documents.".format(i + 1)
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# <FILESEP>
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import matplotlib.pyplot as plot
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from utilities import *
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from distanceMetrics import HausdorffDist,blurredDistance,asymmetricBlurredDistance
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from GA import GeneticAlgorithm
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from render import render,animateMatrices
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from language import *
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from random import random,choice,seed
|
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