From e5c364a28f1cef3f69e088fbd118197be6a3283b Mon Sep 17 00:00:00 2001
From: Prakhar Rathi <38958532+prakharrathi25@users.noreply.github.com>
Date: Thu, 1 Oct 2020 12:56:39 +0530
Subject: [PATCH] Python Public Engine File added

Added a Python engine which connects with react and renders the platform open to processing on Flask.
---
 public_python_engine.py | 130 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 public_python_engine.py

diff --git a/public_python_engine.py b/public_python_engine.py
new file mode 100644
index 0000000..14da099
--- /dev/null
+++ b/public_python_engine.py
@@ -0,0 +1,130 @@
+import utils
+import torch
+from tqdm import tqdm
+import torch.nn as nn
+import numpy as np
+
+def loss_fn(o1,o2,t1,t2):
+    l = nn.CrossEntropyLoss()
+    
+    loss_s = l(o1,t1)
+    loss_e = l(o2,t2)
+    return loss_s+loss_e
+
+    
+def train_fn(data_loader,model,optimizer,device,scheduler):
+    model.train()
+    losses = utils.AverageMeter()
+    jaccard = utils.AverageMeter()
+    tk0 = tqdm(data_loader,total = len(data_loader))
+    for bi,d in enumerate(tk0):
+        ids = d['ids']
+        offsets = d['offsets']
+        orig_tweet = d['orig_tweet']
+        orig_selected = d['orig_selected_text']
+        token_type_ids = d['token_type_ids']
+        sentiments = d['orig_sentiment']
+        mask = d['mask']
+        target_start = d['targets_start']
+        target_end = d['targets_end']
+       
+                
+        ids = ids.to(device,dtype = torch.long)
+        token_type_ids = token_type_ids.to(device,dtype = torch.long)
+        mask = mask.to(device,dtype = torch.long)
+        target_start = target_start.to(device,dtype = torch.long)
+        target_end = target_end.to(device,dtype = torch.long)
+       
+        
+        model.zero_grad()    
+
+        out_start,out_end = model(
+                ids,
+                mask,
+                token_type_ids
+                )
+         
+        loss = loss_fn(out_start,out_end,target_start,target_end)
+        loss.backward()
+        optimizer.step()
+        scheduler.step()
+
+        out_start = torch.softmax(out_start,dim = 1).cpu().detach().numpy()
+        out_end = torch.softmax(out_end,dim = 1).cpu().detach().numpy()
+        
+            
+        jac_scores = []
+       # print(sentiment)
+       # print(offsets,len(offsets),type(offsets))
+        for j,tweet in enumerate(orig_tweet):
+          #  print(j)
+            offset = offsets[j]
+            selected_text = orig_selected[j]
+            sentiment = sentiments[j]
+            idx_start = np.argmax(out_start[j,:])
+            idx_end = np.argmax(out_end[j,:])
+            _,jac = utils.calculate_jaccard(tweet,offset,selected_text,
+                                            idx_start,idx_end,sentiment)
+            jac_scores.append(jac)
+            
+       
+            
+        jaccard.update(np.mean(jac_scores),ids.size(0))
+        losses.update(loss.item(),ids.size(0))
+        tk0.set_postfix(loss = losses.avg,jaccard = jaccard.avg)
+        
+def eval_fn(data_loader,model,device):
+    model.eval()
+    losses = utils.AverageMeter()
+    jaccard = utils.AverageMeter()
+    with torch.no_grad():
+        losses = utils.AverageMeter()
+        jaccard = utils.AverageMeter()
+        tk0 = tqdm(data_loader,total = len(data_loader))
+        for bi,d in enumerate(tk0):
+            ids = d['ids']
+            offsets = d['offsets']         
+            orig_selected = d['orig_selected_text']
+            token_type_ids = d['token_type_ids']
+            sentiments = d['orig_sentiment']
+            mask = d['mask']
+            target_start = d['targets_start']
+            target_end = d['targets_end']
+            orig_tweet = d['orig_tweet']
+            
+            
+            ids = ids.to(device,dtype = torch.long)
+            token_type_ids = token_type_ids.to(device,dtype = torch.long)
+            mask = mask.to(device,dtype = torch.long)
+            target_start = target_start.to(device,dtype = torch.long)
+            target_end = target_end.to(device,dtype = torch.long)
+           
+            out_start,out_end = model(
+                    ids,
+                    mask,
+                    token_type_ids
+                    )
+             
+            loss = loss_fn(out_start,out_end,target_start,target_end)
+    
+            out_start = torch.softmax(out_start,dim = 1).cpu().detach().numpy()
+            out_end = torch.softmax(out_end,dim = 1).cpu().detach().numpy()
+           # print(out_start.shape,out_end.shape)
+            jac_scores = []
+            # print(offsets,len(offsets),type(offsets))
+            for j,tweet in enumerate(orig_tweet):
+                offset = offsets[j]
+                selected_text = orig_selected[j]
+                idx_start = np.argmax(out_start[j,:])
+                sentiment = sentiments[j]
+                idx_end = np.argmax(out_end[j,:])
+                _,jac = utils.calculate_jaccard(tweet,offset,selected_text,
+                                                idx_start,idx_end,sentiment)
+                jac_scores.append(jac)
+                
+            jaccard.update(np.mean(jac_scores),ids.size(0))
+            losses.update(loss.item(),ids.size(0))
+            tk0.set_postfix(loss = losses.avg,jaccard = jaccard.avg)
+                            
+            return np.mean(jac_scores)            
+