sacred

evaluation.Jenkinsfile

@@ -8,7 +8,7 @@ pipeline {
             steps {
                 git 'https://git.wmi.amu.edu.pl/s434749/ium_434749.git'
                 copyArtifacts fingerprintArtifacts: true, projectName: 's434749-training', selector: lastSuccessful()
-                sh 'python3 train_model.py eval'
+                sh 'python3 train_model.py with "mode=eval"'
                 script{
                     def results = readFile "${env.WORKSPACE}/results.txt"
                 }
@@ -17,7 +17,7 @@ pipeline {
             post {
                 success {
                     emailext body: 'Evaluation of CNN for english phonetic embeddings has finished successfully!\n'+results, subject: 's434749 evaluation finished', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
-                    archiveArtifacts 'results.txt'
+                    archiveArtifacts 'results.txt, sacred_file_observer'
                 }
             }
         }

train-model.Jenkinsfile

@@ -8,13 +8,13 @@ pipeline {
             steps {
                 git 'https://git.wmi.amu.edu.pl/s434749/ium_434749.git'
                 copyArtifacts fingerprintArtifacts: true, projectName: 's434749-create-dataset', selector: lastSuccessful()
-                sh 'python3 train_model.py train'
+                sh 'python3 train_model.py'
             }
 
             post {
                 success {
                     emailext body: 'Training of CNN for english phonetic embeddings has finished successfully', subject: 's434749 training finished', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
-                    archiveArtifacts 'cnn.pth'
+                    archiveArtifacts 'cnn.pth,sacred_file_observer'
                 }
             }
         }

train_model.py

@@ -9,6 +9,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
+from sacred.observers import FileStorageObserver, MongoObserver
 from torch.utils.data import Dataset, DataLoader
 import re
 import random
@@ -16,60 +17,22 @@ import os
 import sys
 from tqdm import tqdm
 from Levenshtein import distance as levenshtein_distance
+from sacred import Experiment
 
-DATA_FILE = 'preprocessed.tsv'
-EPOCHS = 14
-TEACHER_FORCING_PROBABILITY = 0.4
-LEARNING_RATE = 0.01
-BATCH_SIZE = 512
-
-DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
-OUT_LOOKUP = ['', 'b', 'a', 'ʊ', 't', 'k', 'ə', 'z', 'ɔ', 'ɹ', 's', 'j', 'u', 'm', 'f', 'ɪ', 'o', 'ɡ', 'ɛ', 'n',
-              'e', 'd',
-              'ɫ', 'w', 'i', 'p', 'ɑ', 'ɝ', 'θ', 'v', 'h', 'æ', 'ŋ', 'ʃ', 'ʒ', 'ð']
-
-IN_LOOKUP = ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
-             'u', 'v', 'w', 'x', 'y', 'z']
-
-IN_ALPHABET = {letter: idx for idx, letter in enumerate(IN_LOOKUP)}
-
-OUT_ALPHABET = {letter: idx for idx, letter in enumerate(OUT_LOOKUP)}
-
-TOTAL_OUT_LEN = 0
-
-DATA: [(torch.tensor, torch.tensor)] = []
-
-TEXT: [str] = []
-
-MAX_LEN = 32
-
-with open(DATA_FILE) as f:
-    for line in f:
-        text, phonemes = line.split("\t")
-        TEXT.append(text)
-        assert len(text) <= MAX_LEN, text
-        text = torch.tensor([IN_ALPHABET[letter] for letter in text], dtype=torch.int)
-        DATA.append((text, phonemes))
-
-
-def collate(batch: [(torch.tensor, str)]):
-    batch_text = torch.zeros((len(batch), len(IN_ALPHABET), MAX_LEN))
-    batch_phonemes = list(map(lambda x: x[1], batch))
-    for i, (sample, _) in enumerate(batch):
-        for chr_pos, index in enumerate(sample):
-            batch_text[i, index, chr_pos] = 1
-    return batch_text, batch_phonemes
-
+ex = Experiment("CNN")
+ex.observers.append(FileStorageObserver('sacred_file_observer'))
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
+                                  db_name='sacred'))
+device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
 
 class CNN(nn.Module):
-    def __init__(self, kernel_size, hidden_layers, channels, embedding_size):
+    def __init__(self, kernel_size, hidden_layers, channels, embedding_size, in_alphabet, max_len):
         super(CNN, self).__init__()
-        self.input_conv = nn.Conv1d(in_channels=len(IN_ALPHABET), out_channels=channels, kernel_size=kernel_size)
+        self.input_conv = nn.Conv1d(in_channels=len(in_alphabet), out_channels=channels, kernel_size=kernel_size)
         self.conv_hidden = nn.ModuleList(
             [nn.Conv1d(in_channels=channels, out_channels=channels, kernel_size=kernel_size) for _ in
              range(hidden_layers)])
-        self.last_layer_size = (MAX_LEN - (kernel_size - 1) * (hidden_layers + 1)) * channels
+        self.last_layer_size = (max_len - (kernel_size - 1) * (hidden_layers + 1)) * channels
         self.lin = nn.Linear(self.last_layer_size, embedding_size)
 
     def forward(self, x):
@@ -83,34 +46,40 @@ class CNN(nn.Module):
         return x
 
 
-outer_bar = tqdm(total=EPOCHS, position=0)
-inner_bar = tqdm(total=len(DATA), position=1)
-
-
 def dist(a: [str], b: [str]):
-    return torch.tensor([levenshtein_distance(a[i], b[i]) for i in range(len(a))], dtype=torch.float, device=DEVICE)
+    return torch.tensor([levenshtein_distance(a[i], b[i]) for i in range(len(a))], dtype=torch.float, device=device)
 
 
-def train_model(model):
+def train_model(model, learning_rate, in_alphabet, max_len, data, epochs, batch_size):
     optimizer = optim.Adam(filter(lambda x: x.requires_grad, model.parameters()),
-                           lr=LEARNING_RATE)
-    loss_snapshots = []
+                           lr=learning_rate)
+    outer_bar = tqdm(total=epochs, position=0)
+    inner_bar = tqdm(total=len(data), position=1)
     outer_bar.reset()
     outer_bar.set_description("Epochs")
-    data_loader = DataLoader(dataset=DATA, drop_last=True,
-                             batch_size=3 * BATCH_SIZE,
+
+    def collate(batch: [(torch.tensor, str)]):
+        batch_text = torch.zeros((len(batch), len(in_alphabet), max_len))
+        batch_phonemes = list(map(lambda x: x[1], batch))
+        for i, (sample, _) in enumerate(batch):
+            for chr_pos, index in enumerate(sample):
+                batch_text[i, index, chr_pos] = 1
+        return batch_text, batch_phonemes
+
+    data_loader = DataLoader(dataset=data, drop_last=True,
+                             batch_size=3 * batch_size,
                              collate_fn=collate,
                              shuffle=True)
-    for epoch in range(EPOCHS):
+    for epoch in range(epochs):
         total_loss = 0
         inner_bar.reset()
 
         for batch_text, batch_phonemes in data_loader:
             optimizer.zero_grad()
-            anchor, positive, negative = batch_text.to(DEVICE).split(BATCH_SIZE)
-            ph_anchor = batch_phonemes[:BATCH_SIZE]
-            ph_positive = batch_phonemes[BATCH_SIZE:2 * BATCH_SIZE]
-            ph_negative = batch_phonemes[2 * BATCH_SIZE:]
+            anchor, positive, negative = batch_text.to(device).split(batch_size)
+            ph_anchor = batch_phonemes[:batch_size]
+            ph_positive = batch_phonemes[batch_size:2 * batch_size]
+            ph_negative = batch_phonemes[2 * batch_size:]
             embedded_anchor = model(anchor)
             embedded_positive = model(positive)
             embedded_negative = model(negative)
@@ -126,11 +95,11 @@ def train_model(model):
                        + (estimated_pos_dist - estimated_neg_dist - (actual_pos_dist - actual_neg_dist)).clip(min=0))
             loss.backward()
             optimizer.step()
-            inner_bar.update(3 * BATCH_SIZE)
+            inner_bar.update(3 * batch_size)
             loss_scalar = loss.item()
             total_loss += loss_scalar
             inner_bar.set_description("loss %.2f" % loss_scalar)
-        loss_snapshots.append(total_loss / len(DATA) * 3)
+        ex.log_scalar("avg_loss", total_loss / len(data) * 3)
         # print()
         # print("Total epoch loss:", total_loss)
         # print("Total epoch avg loss:", total_loss / TOTAL_TRAINING_OUT_LEN)
@@ -142,46 +111,99 @@ def train_model(model):
         outer_bar.update(1)
 
 
-def evaluate_monte_carlo(model, repeats):
+def evaluate_monte_carlo(model, repeats, data, batch_size, in_alphabet, max_len):
     with torch.no_grad():
         i = 0
         diff = 0
-        outer_bar.reset(total=repeats)
+        outer_bar = tqdm(total=repeats, position=0)
+        inner_bar = tqdm(total=len(data), position=1)
         outer_bar.set_description("Epochs")
+
+        def collate(batch: [(torch.tensor, str)]):
+            batch_text = torch.zeros((len(batch), len(in_alphabet), max_len))
+            batch_phonemes = list(map(lambda x: x[1], batch))
+            for i, (sample, _) in enumerate(batch):
+                for chr_pos, index in enumerate(sample):
+                    batch_text[i, index, chr_pos] = 1
+            return batch_text, batch_phonemes
+
         for _ in range(repeats):
-            data_loader = DataLoader(dataset=DATA, drop_last=True,
-                                     batch_size=2 * BATCH_SIZE,
+            data_loader = DataLoader(dataset=data, drop_last=True,
+                                     batch_size=2 * batch_size,
                                      collate_fn=collate,
                                      shuffle=True)
             inner_bar.reset()
             for batch_text, batch_phonemes in data_loader:
-                positive, negative = batch_text.to(DEVICE).split(BATCH_SIZE)
-                ph_positive = batch_phonemes[0:BATCH_SIZE]
-                ph_negative = batch_phonemes[BATCH_SIZE:]
+                positive, negative = batch_text.to(device).split(batch_size)
+                ph_positive = batch_phonemes[0:batch_size]
+                ph_negative = batch_phonemes[batch_size:]
                 embedded_positive = model(positive)
                 embedded_negative = model(negative)
                 estimated_dist = torch.linalg.norm(embedded_negative - embedded_positive, dim=1)
                 actual_dist = dist(ph_negative, ph_positive)
                 diff += sum(abs(estimated_dist - actual_dist))
-                i += BATCH_SIZE
-                inner_bar.update(2 * BATCH_SIZE)
+                i += batch_size
+                inner_bar.update(2 * batch_size)
             outer_bar.update(1)
         with open('results.txt', 'w+') as r:
             print("Average estimation error " + str(diff.item() / i))
             r.write("Average estimation error " + str(diff.item() / i) + "\n")
+            ex.log_scalar("avg_estim_error", diff.item() / i)
 
 
-cnn = CNN(kernel_size=3, hidden_layers=14, channels=MAX_LEN, embedding_size=MAX_LEN).to(DEVICE)
-if os.path.isfile('cnn.pth'):
-    cnn.load_state_dict(torch.load('cnn.pth', map_location=torch.device('cpu')))
-else:
-    if len(sys.argv) > 1 and sys.argv[1] == 'train':
-        train_model(cnn)
-        torch.save(cnn.state_dict(), 'cnn.pth')
+@ex.config
+def cfg():
+    kernel_size = 3
+    hidden_layers = 14
+    data_file = 'preprocessed.tsv'
+    epochs = 14
+    mode = 'train'
+    teacher_forcing_probability = 0.4
+    learning_rate = 0.01
+    batch_size = 512
+    max_len = 32
+    total_out_len = 0
+    model_file = 'cnn.pth'
+    out_lookup = ['', 'b', 'a', 'ʊ', 't', 'k', 'ə', 'z', 'ɔ', 'ɹ', 's', 'j', 'u', 'm', 'f', 'ɪ', 'o', 'ɡ', 'ɛ', 'n',
+                  'e', 'd',
+                  'ɫ', 'w', 'i', 'p', 'ɑ', 'ɝ', 'θ', 'v', 'h', 'æ', 'ŋ', 'ʃ', 'ʒ', 'ð']
+    in_lookup = ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
+                 'u', 'v', 'w', 'x', 'y', 'z']
+
+
+@ex.automain
+def run(kernel_size, hidden_layers, data_file, epochs, teacher_forcing_probability, learning_rate, batch_size, max_len,
+        total_out_len, model_file, out_lookup, in_lookup, mode):
+    in_alphabet = {letter: idx for idx, letter in enumerate(in_lookup)}
+
+    out_alphabet = {letter: idx for idx, letter in enumerate(out_lookup)}
+
+    data: [(torch.tensor, torch.tensor)] = []
+
+    texts: [str] = []
+
+    with open(data_file) as f:
+        for line in f:
+            text, phonemes = line.split("\t")
+            texts.append(text)
+            assert len(text) <= max_len, text
+            text = torch.tensor([in_alphabet[letter] for letter in text], dtype=torch.int)
+            data.append((text, phonemes))
+
+    cnn = CNN(kernel_size=kernel_size, hidden_layers=hidden_layers, channels=max_len, embedding_size=max_len,
+              in_alphabet=in_alphabet, max_len=max_len).to(device)
+    if os.path.isfile(model_file):
+        cnn.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
     else:
-        print("cnn.pth missing!")
-        exit(2)
+        if mode == 'train':
+            train_model(cnn, learning_rate, in_alphabet, max_len, data, epochs, batch_size)
+            torch.save(cnn.state_dict(), model_file)
+            ex.add_artifact(model_file)
+        else:
+            print(model_file + " missing!")
+            exit(2)
+
+    if mode == 'eval':
+        cnn.eval()
+        evaluate_monte_carlo(cnn, 1, data, batch_size, in_alphabet, max_len)
 
-if len(sys.argv) > 1 and sys.argv[1] == 'eval':
-    cnn.eval()
-    evaluate_monte_carlo(cnn, 1)