from __future__ import absolute_import, division, print_function, unicode_literals
import json
import math
import time
import os
import torch
import loaders
import models
import options
import eval_simmc_agent as evaluation
from tools import support
# Arguments.
args = options.read_command_line()
# Dataloader.
dataloader_args = {
"single_pass": False,
"shuffle": True,
"data_read_path": args["train_data_path"],
"get_retrieval_candidates": False
}
dataloader_args.update(args)
train_loader = loaders.DataloaderSIMMC(dataloader_args)
args.update(train_loader.get_data_related_arguments())
# Initiate the loader for val (DEV) data split.
if args["eval_data_path"]:
dataloader_args = {
"single_pass": True,
gt_relevance_round_id - 1, :]
ndcg.observe(output, gt_relevance)
batch_idx += 1
dialog_encoder.train()
print("tot eval batches", batch_idx)
all_metrics = {}
all_metrics.update(sparse_metrics.retrieve(reset=True))
all_metrics.update(ndcg.retrieve(reset=True))
return all_metrics
if __name__ == '__main__':
params = options.read_command_line()
os.makedirs('checkpoints', exist_ok=True)
if not os.path.exists(params['save_path']):
os.mkdir(params['save_path'])
viz = VisdomVisualize(enable=bool(params['enable_visdom']),
env_name=params['visdom_env'],
server=params['visdom_server'],
port=params['visdom_server_port'])
pprint.pprint(params)
viz.addText(pprint.pformat(params, indent=4))
dataset = VisdialDataset(params)
dataset.split = 'train'
dataloader = DataLoader(
dataset,