103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
# 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 torch
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import torch.nn.functional as F
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import os
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from .Vocabulary import Vocabulary
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from Utils import Visdom
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from Utils import universal as U
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class NLPTask(torch.utils.data.Dataset):
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def __init__(self):
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super(NLPTask, self).__init__()
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self.my_dir = os.path.abspath(os.path.dirname(__file__))
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self.cache_dir = os.path.join(self.my_dir, "cache")
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if not os.path.isdir(self.cache_dir):
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os.makedirs(self.cache_dir)
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self.vocabulary = self._load_vocabulary()
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self._preview = None
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def _load_vocabulary(self):
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cache_file = os.path.join(self.cache_dir, "vocabulary.pth")
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if not os.path.isfile(cache_file):
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print("WARNING: Vocabulary not found. Removing cached files.")
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for f in os.listdir(self.cache_dir):
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f = os.path.join(self.cache_dir, f)
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if f.endswith(".pth"):
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print(" "+f)
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os.remove(f)
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return Vocabulary()
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else:
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return torch.load(cache_file)
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def save_vocabulary(self):
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cache_file = os.path.join(self.cache_dir, "vocabulary.pth")
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if os.path.isfile(cache_file):
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os.remove(cache_file)
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torch.save(self.vocabulary, cache_file)
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def loss(self, net_output, target):
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s = list(net_output.size())
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return F.cross_entropy(net_output.view([s[0]*s[1], s[2]]), target.view([-1]), ignore_index=0,
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reduction='sum')/s[0]
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def generate_preview_text(self, data, net_output):
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input = U.to_numpy(data["input"][0])
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reference = U.to_numpy(data["output"][0])
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net_out = U.argmax(net_output[0], -1)
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net_out = U.to_numpy(net_out)
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res = ""
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start_index = 0
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for i in range(input.shape[0]):
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if reference[i] != 0:
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if start_index < i:
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end_index = i
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while end_index>start_index and input[end_index]==0:
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end_index -= 1
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if end_index>start_index:
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sentence = " ".join(self.vocabulary.indices_to_sentence(input[start_index:i].tolist())). \
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replace(" .", ".").replace(" ,", ",").replace(" ?", "?").split(". ")
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sentence = ". ".join([s.capitalize() for s in sentence])
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res += sentence + "<br>"
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start_index = i + 1
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match = reference[i] == net_out[i]
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res += "<b><font color=\"%s\">%s [%s]</font><br></b>" % ("green" if match else "red",
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self.vocabulary.indices_to_sentence(
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[net_out[i]])[0],
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self.vocabulary.indices_to_sentence(
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[reference[i]])[0])
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return res
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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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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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def set_dump_dir(self, dir):
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pass |