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app.py +31 -0
translation/__init__.py +2 -0
translation/__pycache__/__init__.cpython-310.pyc +0 -0
translation/__pycache__/inference.cpython-310.pyc +0 -0
translation/__pycache__/model.cpython-310.pyc +0 -0
translation/inference.py +65 -0
translation/model.pth +3 -0
translation/model.py +47 -0
translation/word2int_cn.json +0 -0
translation/word2int_en.json +1 -0

app.py ADDED Viewed

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+import gradio as gr
+import torch
+from translation import translate, load_model, load_vocab
+MAX_SEQ_LEN = 60
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+MODEL_PATH = "./translation/model.pth"  # 模型权重路径
+SRC_VOCAB_PATH = "./translation/word2int_en.json"  # 英文词汇表路径
+TGT_VOCAB_PATH = "./translation/word2int_cn.json"  # 中文词汇表路径
+# 加载词汇表
+src_vocab = load_vocab(SRC_VOCAB_PATH)
+tgt_vocab = load_vocab(TGT_VOCAB_PATH)
+# 加载模型
+model = load_model(MODEL_PATH, len(src_vocab), len(tgt_vocab))
+# 翻译函数包装为 Gradio 接口
+def translate_sentence(input_sentence):
+    return translate(model, input_sentence, src_vocab, tgt_vocab, MAX_SEQ_LEN)
+# 创建 Gradio 接口
+iface = gr.Interface(
+    fn=translate_sentence,
+    inputs=gr.Textbox(lines=2, placeholder="Enter English sentence here..."),
+    outputs=gr.Textbox(),
+    title="NLP作业：基于Tranformer的机器翻译系统",
+    description="输入英文输出中文喵",
+)
+# 启动 Gradio 应用
+iface.launch()

translation/__init__.py ADDED Viewed

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1	+ from .inference import translate
2	+ from .inference import *

translation/__pycache__/__init__.cpython-310.pyc ADDED Viewed

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translation/inference.py ADDED Viewed

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+import torch
+import json
+import torch.nn as nn
+from .model import TransformerModel  # 确保与训练代码的模型定义一致
+# 配置参数
+MAX_SEQ_LEN = 60
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+MODEL_PATH = "./results/model/model5.pth"  # 模型权重路径
+SRC_VOCAB_PATH = "word2int_en.json"  # 英文词汇表路径
+TGT_VOCAB_PATH = "word2int_cn.json"  # 中文词汇表路径
+# 加载词汇表
+def load_vocab(file_path):
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+# 编码输入句子
+def encode_sentence(sentence, vocab, max_len):
+    tokens = sentence.split()
+    ids = [vocab.get(token, vocab["<UNK>"]) for token in tokens]
+    ids = [vocab["<BOS>"]] + ids[:max_len - 2] + [vocab["<EOS>"]]
+    ids += [vocab["<PAD>"]] * (max_len - len(ids))
+    return torch.tensor(ids, dtype=torch.long).unsqueeze(0).to(DEVICE)  # 添加 batch 维度
+# 解码输出句子
+def decode_sentence(ids, vocab):
+    reverse_vocab = {idx: word for word, idx in vocab.items()}
+    tokens = [reverse_vocab[id] for id in ids if id not in {vocab["<PAD>"], vocab["<BOS>"], vocab["<EOS>"]}]
+    return "".join(tokens)  # 中文不需要空格
+# 加载模型
+def load_model(model_path, src_vocab_size, tgt_vocab_size):
+    model = TransformerModel(src_vocab_size, tgt_vocab_size).to(DEVICE)
+    model.load_state_dict(torch.load(model_path, map_location=DEVICE))
+    model.eval()
+    return model
+# 翻译函数
+def translate(model, sentence, src_vocab, tgt_vocab, max_len):
+    # 编码输入句子
+    src_tensor = encode_sentence(sentence, src_vocab, max_len)
+    # 初始化目标序列为 <BOS>
+    tgt_tensor = torch.tensor([tgt_vocab["<BOS>"]], dtype=torch.long).unsqueeze(0).to(DEVICE)
+    for _ in range(max_len):
+        # 生成目标序列的 mask
+        tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt_tensor.size(1)).to(DEVICE)
+        # 推理得到输出
+        output = model(src_tensor, tgt_tensor, tgt_mask=tgt_mask)
+        # 取最后一个时间步的预测结果
+        next_token = output[:, -1, :].argmax(dim=-1).item()
+        # 将预测的 token 添加到目标序列中
+        tgt_tensor = torch.cat([tgt_tensor, torch.tensor([[next_token]], dtype=torch.long).to(DEVICE)], dim=1)
+        # 如果预测到 <EOS>，停止生成
+        if next_token == tgt_vocab["<EOS>"]:
+            break
+    # 解码目标序列为句子
+    return decode_sentence(tgt_tensor.squeeze(0).tolist(), tgt_vocab)

translation/model.pth ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4e64f5c7ea61f20313e99150dc7ff90e8353acae6dc86de2140472524562554
+size 592222839

translation/model.py ADDED Viewed

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+import torch
+import torch.nn as nn
+# ========== 配置参数 ==========
+BATCH_SIZE = 128
+EPOCHS = 50
+LEARNING_RATE = 1e-4
+MAX_SEQ_LEN = 60
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+D_MODEL = 512
+N_HEAD = 8
+NUM_LAYERS = 6
+DIM_FEEDFORWARD = 2048
+# ========== 数据加载 ==========
+class TransformerModel(nn.Module):
+    def __init__(self, src_vocab_size, tgt_vocab_size, d_model=D_MODEL, nhead=N_HEAD, num_layers=NUM_LAYERS, dim_feedforward=DIM_FEEDFORWARD):
+        super(TransformerModel, self).__init__()
+        self.src_embedding = nn.Embedding(src_vocab_size, d_model)
+        self.tgt_embedding = nn.Embedding(tgt_vocab_size, d_model)
+        self.positional_encoding = nn.Parameter(torch.zeros(1, MAX_SEQ_LEN, d_model))
+        self.transformer = nn.Transformer(
+            d_model=d_model,
+            nhead=nhead,
+            num_encoder_layers=num_layers,
+            num_decoder_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=0.1,
+        )
+        self.fc_out = nn.Linear(d_model, tgt_vocab_size)
+        self.d_model = d_model
+    def forward(self, src, tgt, src_mask=None, tgt_mask=None, src_padding_mask=None, tgt_padding_mask=None):
+        src_emb = self.src_embedding(src) * (self.d_model ** 0.5) + self.positional_encoding[:, :src.size(1), :]
+        tgt_emb = self.tgt_embedding(tgt) * (self.d_model ** 0.5) + self.positional_encoding[:, :tgt.size(1), :]
+        output = self.transformer(
+            src_emb.permute(1, 0, 2),
+            tgt_emb.permute(1, 0, 2),
+            src_mask=src_mask,
+            tgt_mask=tgt_mask,
+            src_key_padding_mask=src_padding_mask,
+            tgt_key_padding_mask=tgt_padding_mask,
+        )
+        return self.fc_out(output.permute(1, 0, 2))

translation/word2int_cn.json ADDED Viewed

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translation/word2int_en.json ADDED Viewed

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+ {"<PAD>": 0, "<BOS>": 1, "<EOS>": 2, "<UNK>": 3, "the": 4, ".": 5, ",": 6, "of": 7, "and": 8, "to": 9, "a": 10, "in": 11, "is": 12, "for": 13, "that": 14, "on": 15, "with": 16, "i": 17, "it": 18, "as": 19, "are": 20, "you": 21, "this": 22, "an": 23, "'s": 24, "be": 25, "by": 26, "was": 27, "at": 28, "-@@": 29, "re@@": 30, "or": 31, "t@@": 32, "from": 33, "have": 34, "``": 35, "s": 36, "ing": 37, "can": 38, "s@@": 39, "p@@": 40, "d@@": 41, "he": 42, "ed": 43, "b@@": 44, "has": 45, "we": 46, "but": 47, "in@@": 48, "g@@": 49, "not": 50, "t": 51, ")": 52, "(": 53, ":": 54, "c@@": 55, "es": 56, "m@@": 57, "k@@": 58, "will": 59, "one": 60, "e@@": 61, "n@@": 62, "his": 63, "o@@": 64, "they": 65, "''": 66, "er": 67, "f@@": 68, "r@@": 69, "l@@": 70, "h@@": 71, "which": 72, "y": 73, "all": 74, "its": 75, "ation": 76, "were": 77, "y@@": 78, "more": 79, "w@@": 80, "un@@": 81, "a@@": 82, "their": 83, "ers": 84, "your": 85, "al": 86, "i@@": 87, "ate": 88, "en@@": 89, "e": 90, "an@@": 91, "v@@": 92, "de@@": 93, "when": 94, "so": 95, "do": 96, "also": 97, "o": 98, "?": 99, "time": 100, "al@@": 101, "if": 102, "new": 103, "there": 104, "ted": 105, "ti@@": 106, "up": 107, "ar@@": 108, "st@@": 109, "u@@": 110, "ting": 111, "said": 112, "out": 113, "my": 114, "ts": 115, "p": 116, "ch@@": 117, "m": 118, "some": 119, "am@@": 120, "about": 121, "been": 122, "our": 123, "co@@": 124, "had": 125, "di@@": 126, "on@@": 127, "people": 128, "as@@": 129, "us": 130, "or@@": 131, "at@@": 132, "into": 133, "system": 134, "it@@": 135, "el@@": 136, "d": 137, "ated": 138, "th@@": 139, "j@@": 140, "who": 141, ";": 142, "dis@@": 143, "like": 144, "ic": 145, "ad@@": 146, "tion": 147, "other": 148, "her": 149, "what": 150, "able": 151, "me": 152, "’": 153, "ro@@": 154, "than": 155, "after": 156, "is@@": 157, "er@@": 158, "ic@@": 159, "red": 160, "china": 161, "ac@@": 162, "ds": 163, "n't": 164, "no": 165, "two": 166, "only": 167, "ap@@": 168, "li@@": 169, "would": 170, "k": 171, "l": 172, "she": 173, "ag@@": 174, "lo@@": 175, "sh@@": 176, "first": 177, "used": 178, "im@@": 179, "ex@@": 180, "con@@": 181, "se@@": 182, "si@@": 183, "g": 184, "ine": 185, "ly": 186, "ri@@": 187, "le@@": 188, "be@@": 189, "z@@": 190, "them": 191, "most": 192, "paper": 193, "use": 194, "-": 195, "then": 196, "these": 197, "ant": 198, "n": 199, "how": 200, "c": 201, "tr@@": 202, "method": 203, "en": 204, "very": 205, "many": 206, "ur@@": 207, "em@@": 208, "ab@@": 209, "es@@": 210, "bo@@": 211, "sp@@": 212, "just": 213, "over": 214, "ations": 215, "ul@@": 216, "u": 217, "x@@": 218, "could": 219, "should": 220, "king": 221, "ol@@": 222, "may": 223, "ent": 224, "op@@": 225, "il@@": 226, "no@@": 227, "ding": 228, "world": 229, "br@@": 230, "pl@@": 231, "such": 232, "ating": 233, "2@@": 234, "years": 235, "ous": 236, "way": 237, "age": 238, "pro@@": 239, "um": 240, "now": 241, "um@@": 242, "to@@": 243, "3@@": 244, "good": 245, "2": 246, "1": 247, "mo@@": 248, "because": 249, "make": 250, "through": 251, "day": 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