270 lines
9.2 KiB
Python
270 lines
9.2 KiB
Python
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# Copyright 2017 Robert Csordas. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# ==============================================================================
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import os
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import glob
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import torch
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from collections import namedtuple
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import numpy as np
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from .NLPTask import NLPTask
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from Utils import Visdom
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Sentence = namedtuple('Sentence', ['sentence', 'answer', 'supporting_facts'])
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class bAbiDataset(NLPTask):
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URL = 'http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz'
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DIR_NAME = "tasks_1-20_v1-2"
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def __init__(self, dirs = ["en-10k"], sets=None, think_steps=0, dir_name=None, name=None):
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super(bAbiDataset, self).__init__()
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self._test_res_win = None
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self._test_plot_win = None
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self._think_steps = think_steps
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if dir_name is None:
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self._download()
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dir_name = os.path.join(self.cache_dir, self.DIR_NAME)
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self.data={}
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for d in dirs:
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self.data[d] = self._load_or_create(os.path.join(dir_name, d))
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self.all_tasks=None
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self.name = name
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self.use(sets=sets)
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def _make_active_list(self, tasks, sets, dirs):
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def verify(name, checker):
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if checker is None:
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return True
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if callable(checker):
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return checker(name)
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elif isinstance(checker, list):
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return name in checker
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else:
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return name==checker
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res = []
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for dirname, setlist in self.data.items():
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if not verify(dirname, dirs):
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continue
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for sname, tasklist in setlist.items():
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if not verify(sname, sets):
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continue
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for task, data in tasklist.items():
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name = task.split("_")[0][2:]
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if not verify(name, tasks):
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continue
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res += [(d, dirname, task, sname) for d in data]
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return res
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def use(self, tasks=None, sets=None, dirs=None):
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self.all_tasks=self._make_active_list(tasks=tasks, sets=sets, dirs=dirs)
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def __len__(self):
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return len(self.all_tasks)
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def _get_seq(self, index):
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return self.all_tasks[index]
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def _seq_to_nn_input(self, seq):
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in_arr = []
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out_arr = []
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hasAnswer = False
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for sentence in seq[0]:
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in_arr += sentence.sentence
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out_arr += [0] * len(sentence.sentence)
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if sentence.answer is not None:
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in_arr += [0] * (len(sentence.answer) + self._think_steps)
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out_arr += [0] * self._think_steps + sentence.answer
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hasAnswer = True
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in_arr = np.asarray(in_arr, np.int64)
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out_arr = np.asarray(out_arr, np.int64)
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return {
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"input": in_arr,
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"output": out_arr,
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"meta": {
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"dir": seq[1],
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"task": seq[2],
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"set": seq[3]
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}
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}
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def __getitem__(self, item):
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seq = self._get_seq(item)
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return self._seq_to_nn_input(seq)
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def _load_or_create(self, directory):
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cache_name = directory.replace("/","_")
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cache_file = os.path.join(self.cache_dir, cache_name+".pth")
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if not os.path.isfile(cache_file):
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print("bAbI: Loading %s" % directory)
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res = self._load_dir(directory)
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print("Write: ", cache_file)
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self.save_vocabulary()
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torch.save(res, cache_file)
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else:
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res = torch.load(cache_file)
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return res
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def _download(self):
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if not os.path.isdir(os.path.join(self.cache_dir, self.DIR_NAME)):
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print(self.URL)
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print("bAbi data not found. Downloading...")
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import requests, tarfile, io
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request = requests.get(self.URL, headers={"User-agent":"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.80 Safari/537.36"})
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decompressed_file = tarfile.open(fileobj=io.BytesIO(request.content), mode='r|gz')
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decompressed_file.extractall(self.cache_dir)
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print("Done")
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def _load_dir(self, directory, parse_name = lambda x: x.split(".")[0], parse_set = lambda x: x.split(".")[0].split("_")[-1]):
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res = {}
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for f in glob.glob(os.path.join(directory, '**', '*.txt'), recursive=True):
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basename = os.path.basename(f)
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task_name = parse_name(basename)
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set = parse_set(basename)
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print("Loading", f)
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s = res.get(set)
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if s is None:
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s = {}
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res[set] = s
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s[task_name] = self._load_task(f, task_name)
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return res
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def _load_task(self, filename, task_name):
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task = []
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currTask = []
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nextIndex = 1
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with open(filename, "r") as f:
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for line in f:
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line = [f.strip() for f in line.split("\t")]
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line[0] = line[0].split(" ")
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i = int(line[0][0])
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line[0] = " ".join(line[0][1:])
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if i!=nextIndex:
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nextIndex = i
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task.append(currTask)
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currTask = []
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isQuestion = len(line)>1
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currTask.append(
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Sentence(self.vocabulary.sentence_to_indices(line[0]), self.vocabulary.sentence_to_indices(line[1].replace(",", " "))
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if isQuestion else None, [int(f) for f in line[2].split(" ")] if isQuestion else None)
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)
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nextIndex += 1
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return task
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def start_test(self):
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return {}
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def veify_result(self, test, data, net_output):
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_, net_output = net_output.max(-1)
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ref = data["output"]
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mask = 1.0 - ref.eq(0).float()
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correct = (torch.eq(net_output, ref).float() * mask).sum(-1)
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total = mask.sum(-1)
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correct = correct.data.cpu().numpy()
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total = total.data.cpu().numpy()
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for i in range(correct.shape[0]):
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task = data["meta"][i]["task"]
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if task not in test:
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test[task] = {"total": 0, "correct": 0}
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d = test[task]
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d["total"] += total[i]
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d["correct"] += correct[i]
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def _ensure_test_wins_exists(self, legend = None):
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if self._test_res_win is None:
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n = (("[" + self.name + "]") if self.name is not None else "")
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self._test_res_win = Visdom.Text("Test results" + n)
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self._test_plot_win = Visdom.Plot2D("Test results" + n, legend=legend)
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elif self._test_plot_win.legend is None:
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self._test_plot_win.set_legend(legend=legend)
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def show_test_results(self, iteration, test):
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res = {k: v["correct"]/v["total"] for k, v in test.items()}
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t = ""
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all_keys = list(res.keys())
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num_keys = [k for k in all_keys if k.startswith("qa")]
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tmp = [i[0] for i in sorted(enumerate(num_keys), key=lambda x:int(x[1][2:].split("_")[0]))]
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num_keys = [num_keys[j] for j in tmp]
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all_keys = num_keys + sorted([k for k in all_keys if not k.startswith("qa")])
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err_precent = [(1.0-res[k]) * 100.0 for k in all_keys]
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n_passed = sum([int(p<=5) for p in err_precent])
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n_total = len(err_precent)
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err_precent = err_precent + [sum(err_precent) / len(err_precent)]
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all_keys += ["mean"]
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for i, k in enumerate(all_keys):
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t += "<font color=\"%s\">%s: <b>%.2f%%</b></font><br>" % ("green" if err_precent[i] <= 5 else "red", k, err_precent[i])
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t += "<br><b>Total: %d of %d passed.</b>" % (n_passed, n_total)
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self._ensure_test_wins_exists(legend=[i.split("_")[0] if i.startswith("qa") else i for i in all_keys])
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self._test_res_win.set(t)
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self._test_plot_win.add_point(iteration, err_precent)
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def state_dict(self):
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if self._test_res_win is not None:
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return {
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"_test_res_win" : self._test_res_win.state_dict(),
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"_test_plot_win": self._test_plot_win.state_dict(),
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}
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else:
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return {}
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def load_state_dict(self, state):
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if state:
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self._ensure_test_wins_exists()
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self._test_res_win.load_state_dict(state["_test_res_win"])
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self._test_plot_win.load_state_dict(state["_test_plot_win"])
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self._test_plot_win.legend = None
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def visualize_preview(self, data, net_output):
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res = self.generate_preview_text(data, net_output)
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res = ("<b><u>%s</u></b><br>" % data["meta"][0]["task"]) + res
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if self._preview is None:
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self._preview = Visdom.Text("Preview")
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self._preview.set(res)
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