COCO-image-captioning-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DMPJcMX1j5hP"
   },
   "source": [
    "# 0.Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3-JnNxgx4uu"
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
    "import torch\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "PAD_IDX=0\n",
    "BERT_CLS_IDX=101\n",
    "BERT_SEP_IDX=102"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hWM0j40w8ek5"
   },
   "source": [
    "# 1.Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 615
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12275,
     "status": "ok",
     "timestamp": 1597110836295,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "hloSSYUC0Y8L",
    "outputId": "68570724-4912-4214-ad39-b40a81abccc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/40/d0/30d5f8d221a0ed981a186c8eb986ce1c94e3a6e87f994eae9f4aa5250217/tokenizers-0.8.1rc1-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
      "\u001b[K     |████████████████████████████████| 3.0MB 52.3MB/s \n",
      "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
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      "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
      "Building wheels for collected packages: sacremoses\n",
      "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=889a02c45d6406f86e657f199840dd0322d9d3ad435cf2a31bb0b96f907c5620\n",
      "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
      "Successfully built sacremoses\n",
      "Installing collected packages: sacremoses, sentencepiece, tokenizers, transformers\n",
      "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc1 transformers-3.0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1196,
     "status": "ok",
     "timestamp": 1597111176480,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "mevwfnDa86rT",
    "outputId": "bb70eef4-c6dc-4fc5-df68-67941e8de5e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import shutil\n",
    "import numpy as np\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from PIL import Image\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "from nltk import word_tokenize\n",
    "from itertools import chain\n",
    "from collections import Counter\n",
    "import json\n",
    "from torchvision.models.detection import fasterrcnn_resnet50_fpn\n",
    "from torchvision.models import vgg19\n",
    "from torch.nn.utils.rnn import pack_sequence\n",
    "from torch.nn.utils.rnn import pack_padded_sequence\n",
    "from torch.nn.utils.rnn import pad_packed_sequence\n",
    "from transformers import BertTokenizer,BertModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 55232,
     "status": "ok",
     "timestamp": 1597110935754,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "tcLmwwEmcBxn",
    "outputId": "66f4acc4-9a8a-4d47-a5dd-b1e2640b539b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "69VcR-BB9U1Y"
   },
   "source": [
    "# 2.Preparing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 245593,
     "status": "ok",
     "timestamp": 1597111429286,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "ZaMDvCHL-1Qm",
    "outputId": "0803a60d-b27d-462f-c895-cbf4245bb03c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    " !unzip drive/My\\ Drive/Colab\\ Data/mscoco.zip -d ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "64T6bj5SjjZU"
   },
   "source": [
    "## 2.0.Vocabulary\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j26aotG4eFXT"
   },
   "outputs": [],
   "source": [
    "BERT_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n",
    "BERT_model = BertModel.from_pretrained('bert-base-uncased',output_hidden_states=True).to(DEVICE)\n",
    "BERT_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d82OHh36xYrL"
   },
   "source": [
    "## 2.1.Batch Preparation Collate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aA0i9KKSxdVu"
   },
   "outputs": [],
   "source": [
    "# Taken from HW4 Part3\n",
    "def pad_tensor(tensor: torch.Tensor, size: int, dim: int=-1):\n",
    "  \"\"\"\n",
    "  tensor: tensor to pad\n",
    "  size: the size to pad to\n",
    "  dim: dimension to pad\n",
    "  returns a new tensor padded to 'size' in dimension 'dim'\n",
    "  \"\"\"\n",
    "  pad_size = list(tensor.shape)\n",
    "  pad_size[dim] = size - tensor.size(dim)\n",
    "  pad = torch.full(pad_size, PAD_IDX, dtype=tensor.dtype, device=tensor.device)\n",
    "  return torch.cat([tensor, pad], dim=dim)\n",
    "\n",
    "# Thanks to https://discuss.pytorch.org/t/dataloader-for-various-length-of-data/6418/8\n",
    "class BatchPrepareCollate:\n",
    "  def __init__(self,vgg,fcnn,mode='train',task='reg2cap'):\n",
    "    self.vgg=vgg\n",
    "    self.fcnn=fcnn\n",
    "    self.mode=mode\n",
    "    self.task=task\n",
    "    self.vgg_transforms=transforms.Compose([\n",
    "                      transforms.Resize((224,224)),\n",
    "                      transforms.ToTensor(),\n",
    "                      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n",
    "  def BatchPrepare_collate(self, batch,mode,task):\n",
    "\n",
    "    if not task=='reg2cap':\n",
    "      batch_image=[transforms.functional.to_tensor(s['image']).to(DEVICE) for s in batch]\n",
    "      with torch.no_grad():\n",
    "        fcnn_res=self.fcnn(batch_image)\n",
    "      \n",
    "      batch_image_boxes=[]\n",
    "      for i in range(len(fcnn_res)):\n",
    "        boxes,scores=fcnn_res[i]['boxes'].detach(),fcnn_res[i]['scores'].detach()\n",
    "        most_likely_boxes=boxes[scores>=(0.75 if mode=='train' else 0.7)]\n",
    "        if most_likely_boxes.size(0)>1:\n",
    "          box_distances_to_true_box=torch.norm(most_likely_boxes-batch[i]['true_box'][:4],dim=1)\n",
    "          nearest_box_idx=torch.argmin(box_distances_to_true_box).item()\n",
    "          other_boxes=most_likely_boxes[[idx for idx in range(most_likely_boxes.shape[0]) if idx!=nearest_box_idx],:]\n",
    "          if mode=='train':\n",
    "            false_box=other_boxes[torch.randint(other_boxes.size(0),(1,)).item()]\n",
    "            false_box_s=torch.tensor([(false_box[2]-false_box[0])*(false_box[3]-false_box[1])],device=DEVICE)  \n",
    "            batch_image_boxes+=[torch.cat((false_box,false_box_s))]\n",
    "          else:\n",
    "            batch_image_boxes+=[torch.cat((other_boxes,((other_boxes[:,2]-other_boxes[:,0])*(other_boxes[:,3]-other_boxes[:,1])).unsqueeze(dim=1)),dim=1)]\n",
    "        else:\n",
    "          dummy_box=torch.zeros((4,),device=DEVICE)\n",
    "          dummy_box_s=torch.zeros((1,),device=DEVICE)\n",
    "          if mode=='train':\n",
    "            batch_image_boxes+=[torch.cat((dummy_box,dummy_box_s))]\n",
    "          else:\n",
    "            batch_image_boxes+=[torch.cat((dummy_box,dummy_box_s)).unsqueeze(dim=0)]\n",
    "      torch.cuda.empty_cache()\n",
    "    \n",
    "    imageVGGInputBatch=torch.stack([s['VGGimagePreLastLayerFeatures'] for s in batch]).to(DEVICE)\n",
    "    batch_image_features=self.vgg['last'](imageVGGInputBatch)\n",
    "\n",
    "    if not (mode=='test' and task=='reg2cap'):\n",
    "      regionVGGInputBatch=torch.stack([s['VGGregionPreLastLayerFeatures'] for s in batch]).to(DEVICE)\n",
    "      batch_region_features=self.vgg['last'](regionVGGInputBatch)\n",
    "\n",
    "    if mode=='train' or task=='reg2cap':\n",
    "      true_spatial_features=torch.stack([s['true_box']/s['spatial_features_denominator'] for s in batch])\n",
    "      batch_true_box_2005_features=torch.cat((batch_image_features,batch_region_features,true_spatial_features),dim=1)\n",
    "\n",
    "    if mode=='train' and task=='cap2reg':\n",
    "      regions_vggInputs=[]\n",
    "      for i in range(len(batch)):\n",
    "        x_tl,y_tl,x_br,y_br=batch_image_boxes[i][:4].cpu().numpy()\n",
    "        h,w=y_br-y_tl,x_br-x_tl\n",
    "        regions_vggInputs+=[self.vgg_transforms(transforms.functional.crop(batch[i]['image'],x_tl,y_tl,h,w)).to(DEVICE)]      \n",
    "      for i in range(len(batch)):\n",
    "        batch_image_boxes[i]=batch_image_boxes[i]/batch[i]['spatial_features_denominator']\n",
    "      batch_false_box_spatial_features=torch.stack(batch_image_boxes)\n",
    "      with torch.no_grad():\n",
    "        preLastLayer=self.vgg['feat-cls'](torch.stack(regions_vggInputs))\n",
    "      region_features=self.vgg['last'](preLastLayer)\n",
    "      batch_false_box_2005_features=torch.cat((batch_image_features,region_features,batch_false_box_spatial_features),dim=1)\n",
    "    elif mode!='train' and task=='cap2reg':\n",
    "      batch_image_regions_2005_features=[]\n",
    "      for i in range(len(batch)):\n",
    "        regions_vggInputs=[]\n",
    "        for j in range(batch_image_boxes[i].size(0)):\n",
    "          x_tl,y_tl,x_br,y_br=batch_image_boxes[i][j][:4].cpu().numpy()\n",
    "          h,w=y_br-y_tl,x_br-x_tl\n",
    "          regions_vggInputs+=[self.vgg_transforms(transforms.functional.crop(batch[i]['image'],x_tl,y_tl,h,w)).to(DEVICE)]\n",
    "        with torch.no_grad():\n",
    "          region_features=self.vgg['last'](self.vgg['feat-cls'](torch.stack(regions_vggInputs)))\n",
    "        image_features=batch_image_features[i].repeat(batch_image_boxes[i].size(0),1)\n",
    "        batch_image_regions_2005_features+=[torch.cat([image_features,region_features,batch_image_boxes[i]/batch[i]['spatial_features_denominator']],dim=1)]\n",
    "\n",
    "    if not (mode=='test' and task=='reg2cap'):\n",
    "      sentences=BERT_tokenizer.batch_encode_plus([sample['sentence'] for sample in batch],\n",
    "                                       add_special_tokens=True,max_length=10,truncation=True,\n",
    "                                       padding=True,return_tensors='pt',return_length=True)\n",
    "      with torch.no_grad():\n",
    "        _,_,embed_hiddens=BERT_model(input_ids=sentences['input_ids'].to(DEVICE),attention_mask=sentences['attention_mask'].to(DEVICE),token_type_ids=sentences['token_type_ids'].to(DEVICE))\n",
    "      embeddings=embed_hiddens[-2]\n",
    "      if mode=='valid' and task=='reg2cap':\n",
    "        padding_mask=(sentences['attention_mask']!=1).to(DEVICE)\n",
    "      if mode!='train' and task=='cap2reg':\n",
    "        sentence_lengths=sentences['length']\n",
    "      sentences=sentences['input_ids'].to(DEVICE)\n",
    "\n",
    "    if mode=='train' and task=='cap2reg':\n",
    "      return sentences,embeddings,batch_true_box_2005_features,batch_false_box_2005_features\n",
    "    elif mode=='train' and task=='reg2cap':\n",
    "      return sentences,embeddings,batch_true_box_2005_features\n",
    "    elif mode=='valid' and task=='reg2cap':\n",
    "      return sentences,embeddings,padding_mask,batch_true_box_2005_features\n",
    "    elif mode=='test' and task=='reg2cap':\n",
    "      return batch_true_box_2005_features\n",
    "    else:\n",
    "      return sentences,embeddings,sentence_lengths,batch_image_regions_2005_features,batch_true_box_2005_features,batch_image_boxes\n",
    "  def __call__(self, batch):\n",
    "    return self.BatchPrepare_collate(batch,self.mode,self.task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6UD4I5NxZlew"
   },
   "source": [
    "## 2.2.Dataset Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oo7k4ZqM9dIy"
   },
   "outputs": [],
   "source": [
    "class CapRegDataset(Dataset):\n",
    "  def __init__(self,vgg,mode='train',task='reg2cap',csv_dir='/content/drive/My Drive/Colab Data/',coco_images_dir='mscoco/'):\n",
    "    self.coco_images_dir=coco_images_dir\n",
    "    self.samples=[]\n",
    "    self.images_features={}\n",
    "    self.mode=mode\n",
    "    self.task=task\n",
    "    self.vgg_transforms=transforms.Compose([\n",
    "                      transforms.Resize((224,224)),\n",
    "                      transforms.ToTensor(),\n",
    "                      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n",
    "    fname='dataset.'+mode+'.csv'\n",
    "    if mode=='test':\n",
    "      if task=='reg2cap':\n",
    "        fname='dataset.test.gen.csv'\n",
    "      else:\n",
    "        fname='dataset.test.comp.csv'\n",
    "    with open(csv_dir+fname) as csvfile:\n",
    "      reader=csv.reader(csvfile)\n",
    "      key_names=next(reader)\n",
    "      bstart=0\n",
    "      length=0\n",
    "      images=[]\n",
    "      images_fnames=[]\n",
    "      regions=[]\n",
    "      for row in reader:\n",
    "        sample=dict(zip(key_names,row))\n",
    "        image=Image.open(self.coco_images_dir+sample['image_fname']).convert('RGB')\n",
    "        if not (mode=='test' and task=='cap2reg'):\n",
    "          sample['box']=[float(x) for x in sample['box'].replace('[','').replace(']','').split()]\n",
    "          x_tl,y_tl,x_br,y_br=sample['box']\n",
    "          regions+=[self.vgg_transforms(transforms.functional.crop(image,x_tl,y_tl,y_br-y_tl,x_br-x_tl))]\n",
    "        if not (sample['image_fname'] in self.images_features):\n",
    "          images+=[self.vgg_transforms(image)]\n",
    "          images_fnames+=[sample['image_fname']]\n",
    "          self.images_features[sample['image_fname']]=image\n",
    "        length+=1\n",
    "        self.samples+=[sample]\n",
    "        if length==256:\n",
    "          im_batch=torch.stack(images).to(DEVICE)\n",
    "          with torch.no_grad():\n",
    "            VGGimageFeatures=torch.unbind(vgg['feat-cls'](im_batch).detach().cpu())\n",
    "          for i in range(len(VGGimageFeatures)):\n",
    "            self.images_features[images_fnames[i]]=VGGimageFeatures[i]\n",
    "          if not (mode=='test' and task=='cap2reg'):\n",
    "            reg_batch=torch.stack(regions).to(DEVICE)\n",
    "            with torch.no_grad():\n",
    "              VGGregionFeatures=torch.unbind(vgg['feat-cls'](reg_batch).detach().cpu())\n",
    "            for i in range(bstart,bstart+length):\n",
    "                self.samples[i]['VGGregionPreLastLayerFeatures']=VGGregionFeatures[i-bstart]\n",
    "          print('preparing {} samples finished!'.format(len(self.samples)))\n",
    "          bstart+=length\n",
    "          length=0\n",
    "          images=[]\n",
    "          regions=[]\n",
    "          images_fnames=[]\n",
    "      if length!=0:\n",
    "        im_batch=torch.stack(images).to(DEVICE)\n",
    "        with torch.no_grad():\n",
    "          VGGimageFeatures=torch.unbind(vgg['feat-cls'](im_batch).detach().cpu())\n",
    "        for i in range(len(VGGimageFeatures)):\n",
    "            self.images_features[images_fnames[i]]=VGGimageFeatures[i]\n",
    "        if not (mode=='test' and task=='cap2reg'):\n",
    "          reg_batch=torch.stack(regions).to(DEVICE)\n",
    "          with torch.no_grad():\n",
    "            VGGregionFeatures=torch.unbind(vgg['feat-cls'](reg_batch).detach().cpu())\n",
    "          for i in range(bstart,bstart+length):\n",
    "              self.samples[i]['VGGregionPreLastLayerFeatures']=VGGregionFeatures[i-bstart]\n",
    "        print('preparing {} samples finished!'.format(len(self.samples)))\n",
    "        bstart+=length\n",
    "        length=0\n",
    "        images=[]\n",
    "        regions=[]\n",
    "        images_fnames=[]\n",
    "      print('Reading Dataset file finished!')     \n",
    "  def __len__(self):\n",
    "    return len(self.samples)\n",
    "  def __getitem__(self, idx):\n",
    "    sample=self.samples[idx]\n",
    "    if not (self.mode=='test' and self.task=='cap2reg'):\n",
    "      x_tl,y_tl,x_br,y_br=sample['box']\n",
    "      s_region=(x_br-x_tl)*(y_br-y_tl)\n",
    "    image=Image.open(self.coco_images_dir+sample['image_fname']).convert('RGB')\n",
    "    W,H=image.size    \n",
    "    denominator=torch.tensor([W,H,W,H,W*H],dtype=torch.float,device=DEVICE)\n",
    "\n",
    "    if (self.mode=='test' and self.task=='cap2reg'):\n",
    "      return {'sentence':sample['sentence'],\n",
    "            'spatial_features_denominator':denominator,\n",
    "            'image':image,\n",
    "            'VGGimagePreLastLayerFeatures':self.images_features[sample['image_fname']]}\n",
    "    elif (self.mode=='test' and self.task=='reg2cap'):\n",
    "      return {'true_box':torch.tensor([x_tl,y_tl,x_br,y_br,s_region],device=DEVICE),\n",
    "            'spatial_features_denominator':denominator,\n",
    "            'image':image,\n",
    "            'VGGimagePreLastLayerFeatures':self.images_features[sample['image_fname']],\n",
    "            'VGGregionPreLastLayerFeatures':sample['VGGregionPreLastLayerFeatures']\n",
    "            }  \n",
    "    return {'sentence':sample['sentence'],\n",
    "            'true_box':torch.tensor([x_tl,y_tl,x_br,y_br,s_region],device=DEVICE),\n",
    "            # 'true_box_size':(y_br-y_tl,x_br-x_tl),\n",
    "            'spatial_features_denominator':denominator,\n",
    "            'image':image,\n",
    "            'VGGimagePreLastLayerFeatures':self.images_features[sample['image_fname']],\n",
    "            'VGGregionPreLastLayerFeatures':sample['VGGregionPreLastLayerFeatures']\n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VpSZxDVw-us5"
   },
   "source": [
    "# 3.Defining Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qvPZy9LC-2e0"
   },
   "outputs": [],
   "source": [
    "class CapRegModel(nn.Module):\n",
    "  def __init__(self,task='reg2cap'):\n",
    "    super(CapRegModel, self).__init__()\n",
    "    self.task=task\n",
    "    self.vocab_size=BERT_tokenizer.vocab_size\n",
    "    self.hidden_size=1024\n",
    "    self.embed_size=768\n",
    "    self.lstm=nn.LSTMCell(2005+self.embed_size,self.hidden_size)\n",
    "    self.pre_vocab_dropout=nn.Dropout(p=0.5)\n",
    "    self.lstm_to_vocab=nn.Linear(self.hidden_size,self.vocab_size,bias=False)\n",
    "  def change_task(self,task):\n",
    "    self.task=task\n",
    "  def forward(self,args):\n",
    "    if self.training and self.task=='cap2reg':\n",
    "      sentences,embeddings,batch_true_box_2005_features,batch_false_box_2005_features=args\n",
    "      combined_batch_input1=[]\n",
    "      combined_batch_input2=[]\n",
    "      sentences_combined=[]\n",
    "      for i in range(batch_true_box_2005_features.size(0)*2):\n",
    "        idx=i//2 if (i%2==0) else (i-1)//2\n",
    "        row=batch_true_box_2005_features[idx] if (i%2==0) else batch_false_box_2005_features[idx]\n",
    "        combined_batch_input1+=[row]\n",
    "        input2=embeddings[idx,:-1]\n",
    "        combined_batch_input2+=[input2]\n",
    "        sentences_combined+=[sentences[idx,1:]]\n",
    "      lstm_input2=torch.stack(combined_batch_input2)#shape after: (2*B,T,embed_size)\n",
    "      lstm_input1=torch.stack(combined_batch_input1)#shape after: (2*B,2005)\n",
    "      h,c=[torch.zeros((batch_true_box_2005_features.size(0)*2,self.hidden_size),dtype=torch.float,device=DEVICE) for i in range(2)]\n",
    "      all_logits=[]\n",
    "      for t in range(sentences.size(1)-1):\n",
    "        lstm_input=torch.cat([lstm_input1,lstm_input2[:,t,:]],dim=1)\n",
    "        h,c=self.lstm(lstm_input,(h,c))\n",
    "        h_dropout=self.pre_vocab_dropout(h)\n",
    "        all_logits+=[self.lstm_to_vocab(h_dropout)]\n",
    "      logits=torch.stack(all_logits,dim=2)#shape after: (B,F,T).to be able to feed into CrossEntropy\n",
    "      loss=F.cross_entropy(logits,torch.stack(sentences_combined),reduction='none',ignore_index=PAD_IDX)#shape after: (B,T)\n",
    "      return torch.mean(torch.sum(loss[[2*i for i in range(batch_true_box_2005_features.size(0))]],dim=1))-torch.mean(torch.sum(loss[[2*i+1 for i in range(batch_false_box_2005_features.size(0))]],dim=1))\n",
    "    elif self.training and self.task=='reg2cap':\n",
    "      sentences,embeddings,batch_true_box_2005_features=args\n",
    "      h,c=[torch.zeros((batch_true_box_2005_features.size(0),self.hidden_size),dtype=torch.float,device=DEVICE) for i in range(2)]\n",
    "      all_logits=[]\n",
    "      for t in range(sentences.size(1)-1):\n",
    "        if t==0:\n",
    "          lstm_input2=embeddings[:,t]\n",
    "        else:\n",
    "          if np.random.uniform()<=0.7:\n",
    "            lstm_input2=embeddings[:,t]\n",
    "          else:\n",
    "            input2=torch.multinomial(F.softmax(logits,dim=1),1)\n",
    "            with torch.no_grad():\n",
    "              lstm_input2=BERT_model(torch.cat((sentences[:,:t],input2),dim=1))[2][-2][:,-1]\n",
    "        lstm_input=torch.cat([batch_true_box_2005_features,lstm_input2],dim=1)\n",
    "        h,c=self.lstm(lstm_input,(h,c))\n",
    "        h_dropout=self.pre_vocab_dropout(h)\n",
    "        logits=self.lstm_to_vocab(h_dropout)\n",
    "        all_logits+=[logits]\n",
    "      loss=F.cross_entropy(torch.stack(all_logits,dim=2),sentences[:,1:],reduction='none',ignore_index=PAD_IDX)#shape after: (B,T)\n",
    "      return torch.mean(torch.sum(loss,dim=1))      \n",
    "    elif self.task=='reg2cap':\n",
    "      sentences,embeddings,padding_mask,batch_true_box_2005_features=args\n",
    "      preds=[]\n",
    "      lstm_input2=embeddings[:,0]\n",
    "      h,c=[torch.zeros((batch_true_box_2005_features.size(0),self.hidden_size),dtype=torch.float,device=DEVICE) for i in range(2)]\n",
    "      all_logits=[]\n",
    "      for t in range(sentences.size(1)-1):\n",
    "        lstm_input=torch.cat([batch_true_box_2005_features,lstm_input2],dim=1)\n",
    "        h,c=self.lstm(lstm_input,(h,c))\n",
    "        h_dropout=self.pre_vocab_dropout(h)\n",
    "        logits=self.lstm_to_vocab(h_dropout)\n",
    "        pred=torch.multinomial(F.softmax(logits,dim=1),1)\n",
    "        preds+=[pred.squeeze(dim=1)]\n",
    "        all_logits+=[logits]\n",
    "        with torch.no_grad():\n",
    "          lstm_input2=BERT_model(torch.cat((torch.stack(preds,dim=1),pred),dim=1))[2][-2][:,-1]\n",
    "      loss=F.cross_entropy(torch.stack(all_logits,dim=2),sentences[:,1:],reduction='none',ignore_index=PAD_IDX)#shape after: (B,T)\n",
    "      preds_padding_restored=torch.stack(preds,dim=1).masked_fill_(padding_mask[:,1:],0)\n",
    "      return torch.mean(torch.sum(loss,dim=1)),preds_padding_restored\n",
    "    else:\n",
    "      #batch_image_regions_2005_features: List(len:batchSize)[Tensor(regionCnt(depends on image),2005)]\n",
    "      sentences,embeddings,sentence_lengths,batch_image_regions_2005_features,batch_true_box_2005_features,batch_image_boxes=args\n",
    "      batch_region_cnts=[s.size(0) for s in batch_image_regions_2005_features]\n",
    "      #input_tensor1 shape: (sigma on B(R_i),T,2005)\n",
    "      lstm_input1=torch.cat([s.unsqueeze(dim=1).expand((s.size(0),sentences.size(1)-1,2005)) for s in batch_image_regions_2005_features],dim=0)\n",
    "      ##input_tensor2 shape: (sigma on B(R_i),T,embed_size)\n",
    "      lstm_input2=torch.cat([embeddings[i][:-1].unsqueeze(dim=0).expand((batch_image_regions_2005_features[i].size(0),sentences.size(1)-1,self.embed_size)) for i in range(sentences.size(0))],dim=0)\n",
    "      lstm_input12=torch.cat([lstm_input1,lstm_input2],dim=2)\n",
    "      h,c=[torch.zeros((sum(batch_region_cnts),self.hidden_size),dtype=torch.float,device=DEVICE) for i in range(2)]\n",
    "      all_log_probs=[]\n",
    "      for t in range(sentences.size(1)-1):\n",
    "        lstm_input=lstm_input12[:,t,:]\n",
    "        h,c=self.lstm(lstm_input,(h,c))\n",
    "        h_dropout=self.pre_vocab_dropout(h)\n",
    "        logits=self.lstm_to_vocab(h_dropout)\n",
    "        all_log_probs+=[F.log_softmax(logits,dim=1)]\n",
    "      log_probs=torch.stack(all_log_probs,dim=1)\n",
    "      regionNum=0\n",
    "      best_regions=[]\n",
    "      for i in range(len(batch_image_regions_2005_features)):\n",
    "        sample_log_probs=log_probs[regionNum:regionNum+batch_region_cnts[i]]\n",
    "        sample_log_probs_unpadded=sample_log_probs[:,:sentence_lengths[i]-1]\n",
    "        sample_regions_log_probs=torch.sum(sample_log_probs_unpadded[:,list(range(sentence_lengths[i]-1)),sentences[i,1:sentence_lengths[i]]],dim=1)\n",
    "        best_regions+=[(batch_image_boxes[i][torch.argmax(sample_regions_log_probs,dim=0)],torch.exp(torch.max(sample_regions_log_probs,dim=0)[0]))]\n",
    "        regionNum+=batch_region_cnts[i]\n",
    "      return best_regions\n",
    "  def generate_caption(self,batch):\n",
    "      batch_true_box_2005_features=batch\n",
    "      max_len_sentence=16\n",
    "      predictions=[]\n",
    "      for i in range(batch_true_box_2005_features.size(0)):\n",
    "        preds=[]\n",
    "        input2=torch.tensor([BERT_CLS_IDX],device=DEVICE)\n",
    "        with torch.no_grad():\n",
    "          lstm_input2=BERT_model(input2)[2][-2][0,-1]\n",
    "        h,c=[torch.zeros((1,self.hidden_size),dtype=torch.float,device=DEVICE) for i in range(2)]\n",
    "        for t in range(max_len_sentence-1):\n",
    "          lstm_input=torch.cat([batch_true_box_2005_features[i],lstm_input2]).unsqueeze(dim=0)\n",
    "          h,c=self.lstm(lstm_input,(h,c))\n",
    "          h_dropout=self.pre_vocab_dropout(h)\n",
    "          logits=self.lstm_to_vocab(h_dropout)\n",
    "          pred=torch.multinomial(F.softmax(logits[0],dim=0),1)\n",
    "          preds+=[pred.item()]\n",
    "          if pred.item()==BERT_SEP_IDX:\n",
    "            break\n",
    "          with torch.no_grad():\n",
    "            lstm_input2=BERT_model(torch.tensor([BERT_CLS_IDX]+preds,device=DEVICE))[2][-2][0,-1]\n",
    "        predictions+=[preds]\n",
    "      return predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mepd6cVB1EHt"
   },
   "source": [
    "# 4.Training Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jC_LpUQP1Rxp"
   },
   "source": [
    "## 4.0. Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k0cnXhLE1Bnw"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE=256\n",
    "LR=0.001\n",
    "EPOCHS=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q3yrDJcR2BXU"
   },
   "source": [
    "## 4.1. Instantiating Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 186,
     "referenced_widgets": [
      "3216e679f12e48329f4ec71a9fbf178e",
      "fc35658b0af743518ea08c67a8911c8b",
      "3533219d65d6445fa490c2de180bd60f",
      "a0fc684ad80948a0b9acc836e5fcaa1e",
      "f7334f2512c04b7faa7ecf985d263a22",
      "060ffc1470c14b6ea950850f4c5ae5be",
      "23cd64bb6eec4dfc85cd8a91606ebd28",
      "eb169b65f3fc4b53b0b022ee9b1e489e",
      "183c0fcd83f747a9a38fe8ce7ecac905",
      "72120e7610144e2a8946016874749cbc",
      "76efb8a5069b43efb6fdc6f6622cbc92",
      "17094e55a7e14abd8009916874c66ccd",
      "377f21f597ac49fdaf3d09ea38bb3b57",
      "085b25f2f902484abd4c02f528e941ab",
      "f0690c9f086d4219870c7ee2b80da5a9",
      "56b0f8a93dd04cee98c3f46d0820af4a"
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 20024,
     "status": "ok",
     "timestamp": 1597111506744,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "wtlAwATzh3Sm",
    "outputId": "65fe907a-7bf0-45fb-bd2d-7539b53101c3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3216e679f12e48329f4ec71a9fbf178e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=167502836.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "183c0fcd83f747a9a38fe8ce7ecac905",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=574673361.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<All keys matched successfully>\n"
     ]
    }
   ],
   "source": [
    "fcnn=fasterrcnn_resnet50_fpn(pretrained=True).to(DEVICE)\n",
    "fcnn.eval()\n",
    "vgg19_model = vgg19(pretrained=True)\n",
    "vggLastLayer=copy.deepcopy(vgg19_model.classifier[6]).to(DEVICE)\n",
    "# for param in vggLastLayer.parameters():\n",
    "#   param.requires_grad = False\n",
    "print(vggLastLayer.load_state_dict(torch.load('drive/My Drive/Colab Data/DL_FP_vgglast_loss40.7.pt')))\n",
    "for param in vgg19_model.parameters():\n",
    "  param.requires_grad = False\n",
    "del vgg19_model.classifier[6]\n",
    "vgg19_model=vgg19_model.to(DEVICE)\n",
    "vgg19_model.eval()\n",
    "vgg={'feat-cls':vgg19_model,'last':vggLastLayer}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1327617,
     "status": "ok",
     "timestamp": 1597112868703,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "SIfJnrW1h5Pu",
    "outputId": "b6f5a7ff-a057-4fba-f932-e11d9726d124"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "trainDS=CapRegDataset(vgg)\n",
    "testDS=CapRegDataset(vgg,mode='valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "28P69Bvrh_9N"
   },
   "outputs": [],
   "source": [
    "trainDL=DataLoader(trainDS,batch_size=BATCH_SIZE,shuffle=True,num_workers=0,collate_fn=BatchPrepareCollate(vgg,fcnn))\n",
    "reg2capTestDL=DataLoader(testDS,batch_size=BATCH_SIZE,shuffle=True,num_workers=0,collate_fn=BatchPrepareCollate(vgg,fcnn,mode='valid'))\n",
    "cap2regTestDL=DataLoader(testDS,batch_size=BATCH_SIZE,shuffle=True,num_workers=0,collate_fn=BatchPrepareCollate(vgg,fcnn,mode='valid',task='cap2reg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3002,
     "status": "ok",
     "timestamp": 1597111519179,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "tNG3Xv5W1h8I",
    "outputId": "46ffa7d7-783b-4929-dbe2-3d100b465b58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<All keys matched successfully>\n"
     ]
    }
   ],
   "source": [
    "model=CapRegModel().to(DEVICE)\n",
    "print(model.load_state_dict(torch.load('drive/My Drive/Colab Data/DL_FP_loss40.7.pt')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5012,
     "status": "ok",
     "timestamp": 1597111523147,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "ikq9oQYZFIC-",
    "outputId": "694a4403-58af-408a-8744-ef5b611ddd14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "optimizer=torch.optim.Adam(list(model.parameters())+list(vgg['last'].parameters()),lr=LR)\n",
    "print(optimizer.load_state_dict(torch.load('drive/My Drive/Colab Data/DL_FP_optim_loss40.7.pt')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QZHjZewjNoqN"
   },
   "source": [
    "## 4.2.Train Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yIEvKycp5W2s"
   },
   "outputs": [],
   "source": [
    "epochs_train_losses=[]\n",
    "epochs_test_losses=[]\n",
    "train_batch_cnt=len(trainDS)//BATCH_SIZE\n",
    "test_batch_cnt=len(testDS)//BATCH_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pQrh6MnNLojM"
   },
   "outputs": [],
   "source": [
    "f=open('drive/My Drive/Colab Data/loss_history.json','r')\n",
    "d=json.loads(f.read())\n",
    "epochs_train_losses=d['train']\n",
    "epochs_test_losses=d['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10502142,
     "status": "ok",
     "timestamp": 1597123370950,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "g8w0edrtNn_V",
    "outputId": "8140901a-4006-4056-f110-303104c2506c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ,Epoch#7 ,batch 1/314 ,avg-loss: 41.01034927368164\n",
      "Train ,Epoch#7 ,batch 11/314 ,avg-loss: 40.75520498102362\n",
      "Train ,Epoch#7 ,batch 21/314 ,avg-loss: 40.69601022629511\n",
      "Train ,Epoch#7 ,batch 31/314 ,avg-loss: 40.585184651036414\n",
      "Train ,Epoch#7 ,batch 41/314 ,avg-loss: 40.55281978700219\n",
      "Train ,Epoch#7 ,batch 51/314 ,avg-loss: 40.56238802741556\n",
      "Train ,Epoch#7 ,batch 61/314 ,avg-loss: 40.56787321997471\n",
      "Train ,Epoch#7 ,batch 71/314 ,avg-loss: 40.59896452997772\n",
      "Train ,Epoch#7 ,batch 81/314 ,avg-loss: 40.59430576842508\n",
      "Train ,Epoch#7 ,batch 91/314 ,avg-loss: 40.591339237087375\n",
      "Train ,Epoch#7 ,batch 101/314 ,avg-loss: 40.60013281944955\n",
      "Train ,Epoch#7 ,batch 111/314 ,avg-loss: 40.601710035994245\n",
      "Train ,Epoch#7 ,batch 121/314 ,avg-loss: 40.59905012777029\n",
      "Train ,Epoch#7 ,batch 131/314 ,avg-loss: 40.61085224879607\n",
      "Train ,Epoch#7 ,batch 141/314 ,avg-loss: 40.636269075650695\n",
      "Train ,Epoch#7 ,batch 151/314 ,avg-loss: 40.62762463803323\n",
      "Train ,Epoch#7 ,batch 161/314 ,avg-loss: 40.624623126865174\n",
      "Train ,Epoch#7 ,batch 171/314 ,avg-loss: 40.64366468909191\n",
      "Train ,Epoch#7 ,batch 181/314 ,avg-loss: 40.65787818023513\n",
      "Train ,Epoch#7 ,batch 191/314 ,avg-loss: 40.65244057540494\n",
      "Train ,Epoch#7 ,batch 201/314 ,avg-loss: 40.656343317743556\n",
      "Train ,Epoch#7 ,batch 211/314 ,avg-loss: 40.65253092327389\n",
      "Train ,Epoch#7 ,batch 221/314 ,avg-loss: 40.66739587222828\n",
      "Train ,Epoch#7 ,batch 231/314 ,avg-loss: 40.678198182737674\n",
      "Train ,Epoch#7 ,batch 241/314 ,avg-loss: 40.669948625366715\n",
      "Train ,Epoch#7 ,batch 251/314 ,avg-loss: 40.65929396124\n",
      "Train ,Epoch#7 ,batch 261/314 ,avg-loss: 40.665139793892926\n",
      "Train ,Epoch#7 ,batch 271/314 ,avg-loss: 40.66542234807876\n",
      "Train ,Epoch#7 ,batch 281/314 ,avg-loss: 40.669738837408424\n",
      "Train ,Epoch#7 ,batch 291/314 ,avg-loss: 40.67148629414667\n",
      "Train ,Epoch#7 ,batch 301/314 ,avg-loss: 40.67545929462015\n",
      "Train ,Epoch#7 ,batch 311/314 ,avg-loss: 40.67512329445008\n",
      "Train ,Epoch#7 ,loss:40.68194062974718\n",
      "Test ,batch 1/19 ,avg-loss: 42.0712776184082\n",
      "Test ,batch 11/19 ,avg-loss: 39.898466630415484\n",
      "Test ,Epoch#7 ,loss:40.05920677185058\n",
      "Train ,Epoch#8 ,batch 1/314 ,avg-loss: 41.35070037841797\n",
      "Train ,Epoch#8 ,batch 11/314 ,avg-loss: 40.620710199529476\n",
      "Train ,Epoch#8 ,batch 21/314 ,avg-loss: 40.55771709623791\n",
      "Train ,Epoch#8 ,batch 31/314 ,avg-loss: 40.59228860178301\n",
      "Train ,Epoch#8 ,batch 41/314 ,avg-loss: 40.60220904466583\n",
      "Train ,Epoch#8 ,batch 51/314 ,avg-loss: 40.59642133525774\n",
      "Train ,Epoch#8 ,batch 61/314 ,avg-loss: 40.60461088086738\n",
      "Train ,Epoch#8 ,batch 71/314 ,avg-loss: 40.606238244285045\n",
      "Train ,Epoch#8 ,batch 81/314 ,avg-loss: 40.58728460618007\n",
      "Train ,Epoch#8 ,batch 91/314 ,avg-loss: 40.586954976176166\n",
      "Train ,Epoch#8 ,batch 101/314 ,avg-loss: 40.56955771871132\n",
      "Train ,Epoch#8 ,batch 111/314 ,avg-loss: 40.59211490390537\n",
      "Train ,Epoch#8 ,batch 121/314 ,avg-loss: 40.58768876130916\n",
      "Train ,Epoch#8 ,batch 131/314 ,avg-loss: 40.5871867114351\n",
      "Train ,Epoch#8 ,batch 141/314 ,avg-loss: 40.586928049723305\n",
      "Train ,Epoch#8 ,batch 151/314 ,avg-loss: 40.58798000512534\n",
      "Train ,Epoch#8 ,batch 161/314 ,avg-loss: 40.57488215191764\n",
      "Train ,Epoch#8 ,batch 171/314 ,avg-loss: 40.5687054862753\n",
      "Train ,Epoch#8 ,batch 181/314 ,avg-loss: 40.558026982934436\n",
      "Train ,Epoch#8 ,batch 191/314 ,avg-loss: 40.56519585254929\n",
      "Train ,Epoch#8 ,batch 201/314 ,avg-loss: 40.56080399698286\n",
      "Train ,Epoch#8 ,batch 211/314 ,avg-loss: 40.5597460416821\n",
      "Train ,Epoch#8 ,batch 221/314 ,avg-loss: 40.56600263431601\n",
      "Train ,Epoch#8 ,batch 231/314 ,avg-loss: 40.57109472761939\n",
      "Train ,Epoch#8 ,batch 241/314 ,avg-loss: 40.57701705995932\n",
      "Train ,Epoch#8 ,batch 251/314 ,avg-loss: 40.59450991505171\n",
      "Train ,Epoch#8 ,batch 261/314 ,avg-loss: 40.581693612752744\n",
      "Train ,Epoch#8 ,batch 271/314 ,avg-loss: 40.58187304563628\n",
      "Train ,Epoch#8 ,batch 281/314 ,avg-loss: 40.57228887972034\n",
      "Train ,Epoch#8 ,batch 291/314 ,avg-loss: 40.56078436939987\n",
      "Train ,Epoch#8 ,batch 301/314 ,avg-loss: 40.55554689679827\n",
      "Train ,Epoch#8 ,batch 311/314 ,avg-loss: 40.54969490762692\n",
      "Train ,Epoch#8 ,loss:40.55426586090572\n",
      "Test ,batch 1/19 ,avg-loss: 41.58204650878906\n",
      "Test ,batch 11/19 ,avg-loss: 40.062822515314274\n",
      "Test ,Epoch#8 ,loss:39.729442024230956\n",
      "Train ,Epoch#9 ,batch 1/314 ,avg-loss: 40.673213958740234\n",
      "Train ,Epoch#9 ,batch 11/314 ,avg-loss: 40.34624654596502\n",
      "Train ,Epoch#9 ,batch 21/314 ,avg-loss: 40.55788439796085\n",
      "Train ,Epoch#9 ,batch 31/314 ,avg-loss: 40.48374200636341\n",
      "Train ,Epoch#9 ,batch 41/314 ,avg-loss: 40.492320549197316\n",
      "Train ,Epoch#9 ,batch 51/314 ,avg-loss: 40.541173972335514\n",
      "Train ,Epoch#9 ,batch 61/314 ,avg-loss: 40.53552321136975\n",
      "Train ,Epoch#9 ,batch 71/314 ,avg-loss: 40.55761670394683\n",
      "Train ,Epoch#9 ,batch 81/314 ,avg-loss: 40.59853443098657\n",
      "Train ,Epoch#9 ,batch 91/314 ,avg-loss: 40.58634315742241\n",
      "Train ,Epoch#9 ,batch 101/314 ,avg-loss: 40.53781143037399\n",
      "Train ,Epoch#9 ,batch 111/314 ,avg-loss: 40.52566961340002\n",
      "Train ,Epoch#9 ,batch 121/314 ,avg-loss: 40.51554608936152\n",
      "Train ,Epoch#9 ,batch 131/314 ,avg-loss: 40.54229584905028\n",
      "Train ,Epoch#9 ,batch 141/314 ,avg-loss: 40.54882566303226\n",
      "Train ,Epoch#9 ,batch 151/314 ,avg-loss: 40.54352407897545\n",
      "Train ,Epoch#9 ,batch 161/314 ,avg-loss: 40.534803662981304\n",
      "Train ,Epoch#9 ,batch 171/314 ,avg-loss: 40.53973196821603\n",
      "Train ,Epoch#9 ,batch 181/314 ,avg-loss: 40.49972449876986\n",
      "Train ,Epoch#9 ,batch 191/314 ,avg-loss: 40.5010020470744\n",
      "Train ,Epoch#9 ,batch 201/314 ,avg-loss: 40.50994472598555\n",
      "Train ,Epoch#9 ,batch 211/314 ,avg-loss: 40.50635074778191\n",
      "Train ,Epoch#9 ,batch 221/314 ,avg-loss: 40.51359480430637\n",
      "Train ,Epoch#9 ,batch 231/314 ,avg-loss: 40.508011012882385\n",
      "Train ,Epoch#9 ,batch 241/314 ,avg-loss: 40.51240474653442\n",
      "Train ,Epoch#9 ,batch 251/314 ,avg-loss: 40.515939963291366\n",
      "Train ,Epoch#9 ,batch 261/314 ,avg-loss: 40.51122625942888\n",
      "Train ,Epoch#9 ,batch 271/314 ,avg-loss: 40.51260627943651\n",
      "Train ,Epoch#9 ,batch 281/314 ,avg-loss: 40.504157572030174\n",
      "Train ,Epoch#9 ,batch 291/314 ,avg-loss: 40.513376216298525\n",
      "Train ,Epoch#9 ,batch 301/314 ,avg-loss: 40.51730124894963\n",
      "Train ,Epoch#9 ,batch 311/314 ,avg-loss: 40.5167516972091\n",
      "Train ,Epoch#9 ,loss:40.51198116484142\n",
      "Test ,batch 1/19 ,avg-loss: 39.231422424316406\n",
      "Test ,batch 11/19 ,avg-loss: 39.86691457575018\n",
      "Test ,Epoch#9 ,loss:39.85040092468262\n",
      "Train ,Epoch#10 ,batch 1/314 ,avg-loss: 40.261268615722656\n",
      "Train ,Epoch#10 ,batch 11/314 ,avg-loss: 40.68037379871715\n",
      "Train ,Epoch#10 ,batch 21/314 ,avg-loss: 40.5908689953032\n",
      "Train ,Epoch#10 ,batch 31/314 ,avg-loss: 40.543458261797504\n",
      "Train ,Epoch#10 ,batch 41/314 ,avg-loss: 40.47348813312809\n",
      "Train ,Epoch#10 ,batch 51/314 ,avg-loss: 40.56115071913775\n",
      "Train ,Epoch#10 ,batch 61/314 ,avg-loss: 40.50870326307953\n",
      "Train ,Epoch#10 ,batch 71/314 ,avg-loss: 40.45660685149716\n",
      "Train ,Epoch#10 ,batch 81/314 ,avg-loss: 40.47173695505401\n",
      "Train ,Epoch#10 ,batch 91/314 ,avg-loss: 40.42386132544214\n",
      "Train ,Epoch#10 ,batch 101/314 ,avg-loss: 40.47074572874768\n",
      "Train ,Epoch#10 ,batch 111/314 ,avg-loss: 40.43672640688784\n",
      "Train ,Epoch#10 ,batch 121/314 ,avg-loss: 40.475905457804025\n",
      "Train ,Epoch#10 ,batch 131/314 ,avg-loss: 40.469065746278254\n",
      "Train ,Epoch#10 ,batch 141/314 ,avg-loss: 40.48131312377064\n",
      "Train ,Epoch#10 ,batch 151/314 ,avg-loss: 40.50292855066969\n",
      "Train ,Epoch#10 ,batch 161/314 ,avg-loss: 40.49311231530231\n",
      "Train ,Epoch#10 ,batch 171/314 ,avg-loss: 40.48751516509474\n",
      "Train ,Epoch#10 ,batch 181/314 ,avg-loss: 40.49951732487968\n",
      "Train ,Epoch#10 ,batch 191/314 ,avg-loss: 40.48689375872387\n",
      "Train ,Epoch#10 ,batch 201/314 ,avg-loss: 40.47122264501467\n",
      "Train ,Epoch#10 ,batch 211/314 ,avg-loss: 40.468445131564025\n",
      "Train ,Epoch#10 ,batch 221/314 ,avg-loss: 40.460627646467806\n",
      "Train ,Epoch#10 ,batch 231/314 ,avg-loss: 40.44504593357895\n",
      "Train ,Epoch#10 ,batch 241/314 ,avg-loss: 40.440865433562344\n",
      "Train ,Epoch#10 ,batch 251/314 ,avg-loss: 40.44979989101213\n",
      "Train ,Epoch#10 ,batch 261/314 ,avg-loss: 40.449838601766416\n",
      "Train ,Epoch#10 ,batch 271/314 ,avg-loss: 40.43967161847217\n",
      "Train ,Epoch#10 ,batch 281/314 ,avg-loss: 40.44065436095105\n",
      "Train ,Epoch#10 ,batch 291/314 ,avg-loss: 40.43265356476774\n",
      "Train ,Epoch#10 ,batch 301/314 ,avg-loss: 40.41981573516744\n",
      "Train ,Epoch#10 ,batch 311/314 ,avg-loss: 40.43286996332395\n",
      "Train ,Epoch#10 ,loss:40.422782050238716\n",
      "Test ,batch 1/19 ,avg-loss: 39.405574798583984\n",
      "Test ,batch 11/19 ,avg-loss: 39.536414406516336\n",
      "Test ,Epoch#10 ,loss:39.59367694854736\n",
      "Train ,Epoch#11 ,batch 1/314 ,avg-loss: 40.78665542602539\n",
      "Train ,Epoch#11 ,batch 11/314 ,avg-loss: 40.40101346102628\n",
      "Train ,Epoch#11 ,batch 21/314 ,avg-loss: 40.41299620128813\n",
      "Train ,Epoch#11 ,batch 31/314 ,avg-loss: 40.42655981740644\n",
      "Train ,Epoch#11 ,batch 41/314 ,avg-loss: 40.4518219552389\n",
      "Train ,Epoch#11 ,batch 51/314 ,avg-loss: 40.43330293543198\n",
      "Train ,Epoch#11 ,batch 61/314 ,avg-loss: 40.391713064224994\n",
      "Train ,Epoch#11 ,batch 71/314 ,avg-loss: 40.349167514854756\n",
      "Train ,Epoch#11 ,batch 81/314 ,avg-loss: 40.37813949584961\n",
      "Train ,Epoch#11 ,batch 91/314 ,avg-loss: 40.3507568862412\n",
      "Train ,Epoch#11 ,batch 101/314 ,avg-loss: 40.33028238126547\n",
      "Train ,Epoch#11 ,batch 111/314 ,avg-loss: 40.34866401741097\n",
      "Train ,Epoch#11 ,batch 121/314 ,avg-loss: 40.36766345441834\n",
      "Train ,Epoch#11 ,batch 131/314 ,avg-loss: 40.36606356205831\n",
      "Train ,Epoch#11 ,batch 141/314 ,avg-loss: 40.35911278690852\n",
      "Train ,Epoch#11 ,batch 151/314 ,avg-loss: 40.36615523281476\n",
      "Train ,Epoch#11 ,batch 161/314 ,avg-loss: 40.35299910077397\n",
      "Train ,Epoch#11 ,batch 171/314 ,avg-loss: 40.37011042812414\n",
      "Train ,Epoch#11 ,batch 181/314 ,avg-loss: 40.362399190828945\n",
      "Train ,Epoch#11 ,batch 191/314 ,avg-loss: 40.345201042934235\n",
      "Train ,Epoch#11 ,batch 201/314 ,avg-loss: 40.32913638821882\n",
      "Train ,Epoch#11 ,batch 211/314 ,avg-loss: 40.33158632251324\n",
      "Train ,Epoch#11 ,batch 221/314 ,avg-loss: 40.33471220542942\n",
      "Train ,Epoch#11 ,batch 231/314 ,avg-loss: 40.322363585104675\n",
      "Train ,Epoch#11 ,batch 241/314 ,avg-loss: 40.307706334284234\n",
      "Train ,Epoch#11 ,batch 251/314 ,avg-loss: 40.31846714399725\n",
      "Train ,Epoch#11 ,batch 261/314 ,avg-loss: 40.31996534793313\n",
      "Train ,Epoch#11 ,batch 271/314 ,avg-loss: 40.33131674734869\n",
      "Train ,Epoch#11 ,batch 281/314 ,avg-loss: 40.34042271515653\n",
      "Train ,Epoch#11 ,batch 291/314 ,avg-loss: 40.350658626490855\n",
      "Train ,Epoch#11 ,batch 301/314 ,avg-loss: 40.34231761286029\n",
      "Train ,Epoch#11 ,batch 311/314 ,avg-loss: 40.35058731128165\n",
      "Train ,Epoch#11 ,loss:40.35541575598338\n",
      "Test ,batch 1/19 ,avg-loss: 40.080284118652344\n",
      "Test ,batch 11/19 ,avg-loss: 39.759276303378016\n",
      "Test ,Epoch#11 ,loss:39.55699691772461\n",
      "Train ,Epoch#12 ,batch 1/314 ,avg-loss: 39.777732849121094\n",
      "Train ,Epoch#12 ,batch 11/314 ,avg-loss: 40.07133587923917\n",
      "Train ,Epoch#12 ,batch 21/314 ,avg-loss: 40.284213474818635\n",
      "Train ,Epoch#12 ,batch 31/314 ,avg-loss: 40.20648808633128\n",
      "Train ,Epoch#12 ,batch 41/314 ,avg-loss: 40.26021315411823\n",
      "Train ,Epoch#12 ,batch 51/314 ,avg-loss: 40.29047042248296\n",
      "Train ,Epoch#12 ,batch 61/314 ,avg-loss: 40.28365838723104\n",
      "Train ,Epoch#12 ,batch 71/314 ,avg-loss: 40.25136012762365\n",
      "Train ,Epoch#12 ,batch 81/314 ,avg-loss: 40.1833709905177\n",
      "Train ,Epoch#12 ,batch 91/314 ,avg-loss: 40.249817774846\n",
      "Train ,Epoch#12 ,batch 101/314 ,avg-loss: 40.265856487916245\n",
      "Train ,Epoch#12 ,batch 111/314 ,avg-loss: 40.29618752969278\n",
      "Train ,Epoch#12 ,batch 121/314 ,avg-loss: 40.3183024067524\n",
      "Train ,Epoch#12 ,batch 131/314 ,avg-loss: 40.31170520345673\n",
      "Train ,Epoch#12 ,batch 141/314 ,avg-loss: 40.28661811774504\n",
      "Train ,Epoch#12 ,batch 151/314 ,avg-loss: 40.280011638110835\n",
      "Train ,Epoch#12 ,batch 161/314 ,avg-loss: 40.274343928935366\n",
      "Train ,Epoch#12 ,batch 171/314 ,avg-loss: 40.29037778977065\n",
      "Train ,Epoch#12 ,batch 181/314 ,avg-loss: 40.28132677868585\n",
      "Train ,Epoch#12 ,batch 191/314 ,avg-loss: 40.27842712402344\n",
      "Train ,Epoch#12 ,batch 201/314 ,avg-loss: 40.27849113881884\n",
      "Train ,Epoch#12 ,batch 211/314 ,avg-loss: 40.29950381455263\n",
      "Train ,Epoch#12 ,batch 221/314 ,avg-loss: 40.29163022278661\n",
      "Train ,Epoch#12 ,batch 231/314 ,avg-loss: 40.28636488150725\n",
      "Train ,Epoch#12 ,batch 241/314 ,avg-loss: 40.2843280016634\n",
      "Train ,Epoch#12 ,batch 251/314 ,avg-loss: 40.29575200479819\n",
      "Train ,Epoch#12 ,batch 261/314 ,avg-loss: 40.30654617835735\n",
      "Train ,Epoch#12 ,batch 271/314 ,avg-loss: 40.31256504692275\n",
      "Train ,Epoch#12 ,batch 281/314 ,avg-loss: 40.32023254068721\n",
      "Train ,Epoch#12 ,batch 291/314 ,avg-loss: 40.32371101510484\n",
      "Train ,Epoch#12 ,batch 301/314 ,avg-loss: 40.331851971901926\n",
      "Train ,Epoch#12 ,batch 311/314 ,avg-loss: 40.334657025107234\n",
      "Train ,Epoch#12 ,loss:40.336434609549386\n",
      "Test ,batch 1/19 ,avg-loss: 38.90126037597656\n",
      "Test ,batch 11/19 ,avg-loss: 39.74178730357777\n",
      "Test ,Epoch#12 ,loss:39.71092853546143\n",
      "Train ,Epoch#13 ,batch 1/314 ,avg-loss: 40.773582458496094\n",
      "Train ,Epoch#13 ,batch 11/314 ,avg-loss: 40.14878220991655\n",
      "Train ,Epoch#13 ,batch 21/314 ,avg-loss: 40.205343155633834\n",
      "Train ,Epoch#13 ,batch 31/314 ,avg-loss: 40.23477997318391\n",
      "Train ,Epoch#13 ,batch 41/314 ,avg-loss: 40.26688896737448\n",
      "Train ,Epoch#13 ,batch 51/314 ,avg-loss: 40.20137495153091\n",
      "Train ,Epoch#13 ,batch 61/314 ,avg-loss: 40.19508274265977\n",
      "Train ,Epoch#13 ,batch 71/314 ,avg-loss: 40.128427156260315\n",
      "Train ,Epoch#13 ,batch 81/314 ,avg-loss: 40.18139205744237\n",
      "Train ,Epoch#13 ,batch 91/314 ,avg-loss: 40.1592162834419\n",
      "Train ,Epoch#13 ,batch 101/314 ,avg-loss: 40.14104642962465\n",
      "Train ,Epoch#13 ,batch 111/314 ,avg-loss: 40.1485912907231\n",
      "Train ,Epoch#13 ,batch 121/314 ,avg-loss: 40.17397235838835\n",
      "Train ,Epoch#13 ,batch 131/314 ,avg-loss: 40.164822381871346\n",
      "Train ,Epoch#13 ,batch 141/314 ,avg-loss: 40.14599279308995\n",
      "Train ,Epoch#13 ,batch 151/314 ,avg-loss: 40.16719247173789\n",
      "Train ,Epoch#13 ,batch 161/314 ,avg-loss: 40.12616729736328\n",
      "Train ,Epoch#13 ,batch 171/314 ,avg-loss: 40.130854757208574\n",
      "Train ,Epoch#13 ,batch 181/314 ,avg-loss: 40.13982779149851\n",
      "Train ,Epoch#13 ,batch 191/314 ,avg-loss: 40.144802672700735\n",
      "Train ,Epoch#13 ,batch 201/314 ,avg-loss: 40.15120664757875\n",
      "Train ,Epoch#13 ,batch 211/314 ,avg-loss: 40.15433080953444\n",
      "Train ,Epoch#13 ,batch 221/314 ,avg-loss: 40.179535395419435\n",
      "Train ,Epoch#13 ,batch 231/314 ,avg-loss: 40.17885873637674\n",
      "Train ,Epoch#13 ,batch 241/314 ,avg-loss: 40.180470715914524\n",
      "Train ,Epoch#13 ,batch 251/314 ,avg-loss: 40.185860394481644\n",
      "Train ,Epoch#13 ,batch 261/314 ,avg-loss: 40.18010236659726\n",
      "Train ,Epoch#13 ,batch 271/314 ,avg-loss: 40.165815332278996\n",
      "Train ,Epoch#13 ,batch 281/314 ,avg-loss: 40.174236704870474\n",
      "Train ,Epoch#13 ,batch 291/314 ,avg-loss: 40.18240150635185\n",
      "Train ,Epoch#13 ,batch 301/314 ,avg-loss: 40.1957066431394\n",
      "Train ,Epoch#13 ,batch 311/314 ,avg-loss: 40.20004662516799\n",
      "Train ,Epoch#13 ,loss:40.202833411807106\n",
      "Test ,batch 1/19 ,avg-loss: 38.89693069458008\n",
      "Test ,batch 11/19 ,avg-loss: 39.09980288418856\n",
      "Test ,Epoch#13 ,loss:39.331372833251955\n",
      "Train ,Epoch#14 ,batch 1/314 ,avg-loss: 38.79486083984375\n",
      "Train ,Epoch#14 ,batch 11/314 ,avg-loss: 40.001839030872695\n",
      "Train ,Epoch#14 ,batch 21/314 ,avg-loss: 39.90121714274088\n",
      "Train ,Epoch#14 ,batch 31/314 ,avg-loss: 39.90217836441532\n",
      "Train ,Epoch#14 ,batch 41/314 ,avg-loss: 39.89573325180426\n",
      "Train ,Epoch#14 ,batch 51/314 ,avg-loss: 39.85179205501781\n",
      "Train ,Epoch#14 ,batch 61/314 ,avg-loss: 39.841574872126344\n",
      "Train ,Epoch#14 ,batch 71/314 ,avg-loss: 39.850550208293214\n",
      "Train ,Epoch#14 ,batch 81/314 ,avg-loss: 39.853832009397905\n",
      "Train ,Epoch#14 ,batch 91/314 ,avg-loss: 39.92025740068038\n",
      "Train ,Epoch#14 ,batch 101/314 ,avg-loss: 39.919622289072166\n",
      "Train ,Epoch#14 ,batch 111/314 ,avg-loss: 39.948297380327105\n",
      "Train ,Epoch#14 ,batch 121/314 ,avg-loss: 39.97697574836163\n",
      "Train ,Epoch#14 ,batch 131/314 ,avg-loss: 39.94871954881508\n",
      "Train ,Epoch#14 ,batch 141/314 ,avg-loss: 39.9809490230912\n",
      "Train ,Epoch#14 ,batch 151/314 ,avg-loss: 39.973742756622514\n",
      "Train ,Epoch#14 ,batch 161/314 ,avg-loss: 39.955787374366146\n",
      "Train ,Epoch#14 ,batch 171/314 ,avg-loss: 39.95127402411567\n",
      "Train ,Epoch#14 ,batch 181/314 ,avg-loss: 39.96175045203109\n",
      "Train ,Epoch#14 ,batch 191/314 ,avg-loss: 39.97253689591173\n",
      "Train ,Epoch#14 ,batch 201/314 ,avg-loss: 39.98362098048575\n",
      "Train ,Epoch#14 ,batch 211/314 ,avg-loss: 39.98483223937699\n",
      "Train ,Epoch#14 ,batch 221/314 ,avg-loss: 39.99360720604253\n",
      "Train ,Epoch#14 ,batch 231/314 ,avg-loss: 39.98831064463694\n",
      "Train ,Epoch#14 ,batch 241/314 ,avg-loss: 40.00234048297296\n",
      "Train ,Epoch#14 ,batch 251/314 ,avg-loss: 40.0052557713482\n",
      "Train ,Epoch#14 ,batch 261/314 ,avg-loss: 40.01656888179852\n",
      "Train ,Epoch#14 ,batch 271/314 ,avg-loss: 40.02896087196041\n",
      "Train ,Epoch#14 ,batch 281/314 ,avg-loss: 40.009541100878735\n",
      "Train ,Epoch#14 ,batch 291/314 ,avg-loss: 40.02205609455961\n",
      "Train ,Epoch#14 ,batch 301/314 ,avg-loss: 40.01754974922864\n",
      "Train ,Epoch#14 ,batch 311/314 ,avg-loss: 40.01636310099022\n",
      "Train ,Epoch#14 ,loss:40.01856936500186\n",
      "Test ,batch 1/19 ,avg-loss: 40.304630279541016\n",
      "Test ,batch 11/19 ,avg-loss: 39.57960267500444\n",
      "Test ,Epoch#14 ,loss:39.51320934295654\n",
      "Train ,Epoch#15 ,batch 1/314 ,avg-loss: 38.774131774902344\n",
      "Train ,Epoch#15 ,batch 11/314 ,avg-loss: 39.42297293923118\n",
      "Train ,Epoch#15 ,batch 21/314 ,avg-loss: 39.60004497709728\n",
      "Train ,Epoch#15 ,batch 31/314 ,avg-loss: 39.66488339824061\n",
      "Train ,Epoch#15 ,batch 41/314 ,avg-loss: 39.76296624904725\n",
      "Train ,Epoch#15 ,batch 51/314 ,avg-loss: 39.78360471538469\n",
      "Train ,Epoch#15 ,batch 61/314 ,avg-loss: 39.751230396208214\n",
      "Train ,Epoch#15 ,batch 71/314 ,avg-loss: 39.857305983422506\n",
      "Train ,Epoch#15 ,batch 81/314 ,avg-loss: 39.859390447169176\n",
      "Train ,Epoch#15 ,batch 91/314 ,avg-loss: 39.86729879693671\n",
      "Train ,Epoch#15 ,batch 101/314 ,avg-loss: 39.92283501955542\n",
      "Train ,Epoch#15 ,batch 111/314 ,avg-loss: 39.90905232472463\n",
      "Train ,Epoch#15 ,batch 121/314 ,avg-loss: 39.91973104555745\n",
      "Train ,Epoch#15 ,batch 131/314 ,avg-loss: 39.92037221311613\n",
      "Train ,Epoch#15 ,batch 141/314 ,avg-loss: 39.954878272739705\n",
      "Train ,Epoch#15 ,batch 151/314 ,avg-loss: 39.973040119701665\n",
      "Train ,Epoch#15 ,batch 161/314 ,avg-loss: 40.00370850178025\n",
      "Train ,Epoch#15 ,batch 171/314 ,avg-loss: 40.018679122478645\n",
      "Train ,Epoch#15 ,batch 181/314 ,avg-loss: 40.027333254313604\n",
      "Train ,Epoch#15 ,batch 191/314 ,avg-loss: 40.04803334980111\n",
      "Train ,Epoch#15 ,batch 201/314 ,avg-loss: 40.031866519605344\n",
      "Train ,Epoch#15 ,batch 211/314 ,avg-loss: 40.02808835721129\n",
      "Train ,Epoch#15 ,batch 221/314 ,avg-loss: 40.03096101510579\n",
      "Train ,Epoch#15 ,batch 231/314 ,avg-loss: 40.021533883495245\n",
      "Train ,Epoch#15 ,batch 241/314 ,avg-loss: 40.02404015885349\n",
      "Train ,Epoch#15 ,batch 251/314 ,avg-loss: 40.0139222620018\n",
      "Train ,Epoch#15 ,batch 261/314 ,avg-loss: 40.00888444454734\n",
      "Train ,Epoch#15 ,batch 271/314 ,avg-loss: 40.00734623419842\n",
      "Train ,Epoch#15 ,batch 281/314 ,avg-loss: 40.00364948422035\n",
      "Train ,Epoch#15 ,batch 291/314 ,avg-loss: 39.995265947584436\n",
      "Train ,Epoch#15 ,batch 301/314 ,avg-loss: 39.99861189376476\n",
      "Train ,Epoch#15 ,batch 311/314 ,avg-loss: 40.013549277253475\n",
      "Train ,Epoch#15 ,loss:40.017036995055186\n",
      "Test ,batch 1/19 ,avg-loss: 40.66665267944336\n",
      "Test ,batch 11/19 ,avg-loss: 39.41133984652433\n",
      "Test ,Epoch#15 ,loss:39.486362075805665\n",
      "Train ,Epoch#16 ,batch 1/314 ,avg-loss: 41.15475082397461\n",
      "Train ,Epoch#16 ,batch 11/314 ,avg-loss: 39.86289457841353\n",
      "Train ,Epoch#16 ,batch 21/314 ,avg-loss: 39.792516254243395\n",
      "Train ,Epoch#16 ,batch 31/314 ,avg-loss: 39.952507634316724\n",
      "Train ,Epoch#16 ,batch 41/314 ,avg-loss: 39.86712441793302\n",
      "Train ,Epoch#16 ,batch 51/314 ,avg-loss: 39.80789775474399\n",
      "Train ,Epoch#16 ,batch 61/314 ,avg-loss: 39.85685198424292\n",
      "Train ,Epoch#16 ,batch 71/314 ,avg-loss: 39.849406443851095\n",
      "Train ,Epoch#16 ,batch 81/314 ,avg-loss: 39.9121875056514\n",
      "Train ,Epoch#16 ,batch 91/314 ,avg-loss: 39.868895562140494\n",
      "Train ,Epoch#16 ,batch 101/314 ,avg-loss: 39.81812577200408\n",
      "Train ,Epoch#16 ,batch 111/314 ,avg-loss: 39.851177043742965\n",
      "Train ,Epoch#16 ,batch 121/314 ,avg-loss: 39.86405903839868\n",
      "Train ,Epoch#16 ,batch 131/314 ,avg-loss: 39.91610155760787\n",
      "Train ,Epoch#16 ,batch 141/314 ,avg-loss: 39.905383657901844\n",
      "Train ,Epoch#16 ,batch 151/314 ,avg-loss: 39.90367540776335\n",
      "Train ,Epoch#16 ,batch 161/314 ,avg-loss: 39.90612529967883\n",
      "Train ,Epoch#16 ,batch 171/314 ,avg-loss: 39.92265143589667\n",
      "Train ,Epoch#16 ,batch 181/314 ,avg-loss: 39.92270424352825\n",
      "Train ,Epoch#16 ,batch 191/314 ,avg-loss: 39.935076169318556\n",
      "Train ,Epoch#16 ,batch 201/314 ,avg-loss: 39.9596836412724\n",
      "Train ,Epoch#16 ,batch 211/314 ,avg-loss: 39.979773878486235\n",
      "Train ,Epoch#16 ,batch 221/314 ,avg-loss: 40.014201496521274\n",
      "Train ,Epoch#16 ,batch 231/314 ,avg-loss: 40.03495742335464\n",
      "Train ,Epoch#16 ,batch 241/314 ,avg-loss: 40.04096312146959\n",
      "Train ,Epoch#16 ,batch 251/314 ,avg-loss: 40.03679620507229\n",
      "Train ,Epoch#16 ,batch 261/314 ,avg-loss: 40.0388789414446\n",
      "Train ,Epoch#16 ,batch 271/314 ,avg-loss: 40.047709194056665\n",
      "Train ,Epoch#16 ,batch 281/314 ,avg-loss: 40.06573160517682\n",
      "Train ,Epoch#16 ,batch 291/314 ,avg-loss: 40.06871861198923\n",
      "Train ,Epoch#16 ,batch 301/314 ,avg-loss: 40.07906790191549\n",
      "Train ,Epoch#16 ,batch 311/314 ,avg-loss: 40.09062751696424\n",
      "Train ,Epoch#16 ,loss:40.08700050475105\n",
      "Test ,batch 1/19 ,avg-loss: 40.411285400390625\n",
      "Test ,batch 11/19 ,avg-loss: 39.64632311734286\n",
      "Test ,Epoch#16 ,loss:39.50515651702881\n",
      "Train ,Epoch#17 ,batch 1/314 ,avg-loss: 39.87848663330078\n",
      "Train ,Epoch#17 ,batch 11/314 ,avg-loss: 39.73595705899325\n",
      "Train ,Epoch#17 ,batch 21/314 ,avg-loss: 39.71133786156064\n",
      "Train ,Epoch#17 ,batch 31/314 ,avg-loss: 39.79972568635018\n",
      "Train ,Epoch#17 ,batch 41/314 ,avg-loss: 39.79926607085437\n",
      "Train ,Epoch#17 ,batch 51/314 ,avg-loss: 39.816372216916555\n",
      "Train ,Epoch#17 ,batch 61/314 ,avg-loss: 39.85666487646885\n",
      "Train ,Epoch#17 ,batch 71/314 ,avg-loss: 39.90294045461735\n",
      "Train ,Epoch#17 ,batch 81/314 ,avg-loss: 39.88519795735677\n",
      "Train ,Epoch#17 ,batch 91/314 ,avg-loss: 39.85510325169825\n",
      "Train ,Epoch#17 ,batch 101/314 ,avg-loss: 39.814872703929936\n",
      "Train ,Epoch#17 ,batch 111/314 ,avg-loss: 39.844302924903666\n",
      "Train ,Epoch#17 ,batch 121/314 ,avg-loss: 39.846409474522616\n",
      "Train ,Epoch#17 ,batch 131/314 ,avg-loss: 39.844771115834476\n",
      "Train ,Epoch#17 ,batch 141/314 ,avg-loss: 39.88867677188089\n",
      "Train ,Epoch#17 ,batch 151/314 ,avg-loss: 39.88902919339818\n",
      "Train ,Epoch#17 ,batch 161/314 ,avg-loss: 39.90713723698018\n",
      "Train ,Epoch#17 ,batch 171/314 ,avg-loss: 39.93352595546789\n",
      "Train ,Epoch#17 ,batch 181/314 ,avg-loss: 39.93518093804628\n",
      "Train ,Epoch#17 ,batch 191/314 ,avg-loss: 39.933002272201456\n",
      "Train ,Epoch#17 ,batch 201/314 ,avg-loss: 39.934929501357956\n",
      "Train ,Epoch#17 ,batch 211/314 ,avg-loss: 39.9469300220363\n",
      "Train ,Epoch#17 ,batch 221/314 ,avg-loss: 39.937691891355215\n",
      "Train ,Epoch#17 ,batch 231/314 ,avg-loss: 39.95438208311667\n",
      "Train ,Epoch#17 ,batch 241/314 ,avg-loss: 39.96268611923787\n",
      "Train ,Epoch#17 ,batch 251/314 ,avg-loss: 39.94395961989444\n",
      "Train ,Epoch#17 ,batch 261/314 ,avg-loss: 39.952475317593276\n",
      "Train ,Epoch#17 ,batch 271/314 ,avg-loss: 39.95537227898066\n",
      "Train ,Epoch#17 ,batch 281/314 ,avg-loss: 39.97351328079387\n",
      "Train ,Epoch#17 ,batch 291/314 ,avg-loss: 39.98023324815678\n",
      "Train ,Epoch#17 ,batch 301/314 ,avg-loss: 39.99190290584121\n",
      "Train ,Epoch#17 ,batch 311/314 ,avg-loss: 40.00385178937023\n",
      "Train ,Epoch#17 ,loss:39.99921997312516\n",
      "Test ,batch 1/19 ,avg-loss: 39.547115325927734\n",
      "Test ,batch 11/19 ,avg-loss: 39.16819104281339\n",
      "Test ,Epoch#17 ,loss:39.60472011566162\n",
      "Train ,Epoch#18 ,batch 1/314 ,avg-loss: 42.7308464050293\n",
      "Train ,Epoch#18 ,batch 11/314 ,avg-loss: 39.8335637179288\n",
      "Train ,Epoch#18 ,batch 21/314 ,avg-loss: 39.67734182448614\n",
      "Train ,Epoch#18 ,batch 31/314 ,avg-loss: 39.710639338339526\n",
      "Train ,Epoch#18 ,batch 41/314 ,avg-loss: 39.748417831048734\n",
      "Train ,Epoch#18 ,batch 51/314 ,avg-loss: 39.74468979181028\n",
      "Train ,Epoch#18 ,batch 61/314 ,avg-loss: 39.738685482838115\n",
      "Train ,Epoch#18 ,batch 71/314 ,avg-loss: 39.673072868669536\n",
      "Train ,Epoch#18 ,batch 81/314 ,avg-loss: 39.64251497056749\n",
      "Train ,Epoch#18 ,batch 91/314 ,avg-loss: 39.62786643059699\n",
      "Train ,Epoch#18 ,batch 101/314 ,avg-loss: 39.60800752545347\n",
      "Train ,Epoch#18 ,batch 111/314 ,avg-loss: 39.67481004869616\n",
      "Train ,Epoch#18 ,batch 121/314 ,avg-loss: 39.65781251261057\n",
      "Train ,Epoch#18 ,batch 131/314 ,avg-loss: 39.63897405143913\n",
      "Train ,Epoch#18 ,batch 141/314 ,avg-loss: 39.66130980363129\n",
      "Train ,Epoch#18 ,batch 151/314 ,avg-loss: 39.638640978478435\n",
      "Train ,Epoch#18 ,batch 161/314 ,avg-loss: 39.65374857742594\n",
      "Train ,Epoch#18 ,batch 171/314 ,avg-loss: 39.64608956498709\n",
      "Train ,Epoch#18 ,batch 181/314 ,avg-loss: 39.65663692832652\n",
      "Train ,Epoch#18 ,batch 191/314 ,avg-loss: 39.6575012606476\n",
      "Train ,Epoch#18 ,batch 201/314 ,avg-loss: 39.66196555521951\n",
      "Train ,Epoch#18 ,batch 211/314 ,avg-loss: 39.668705095047066\n",
      "Train ,Epoch#18 ,batch 221/314 ,avg-loss: 39.67563575641063\n",
      "Train ,Epoch#18 ,batch 231/314 ,avg-loss: 39.665163114473415\n",
      "Train ,Epoch#18 ,batch 241/314 ,avg-loss: 39.68903553436406\n",
      "Train ,Epoch#18 ,batch 251/314 ,avg-loss: 39.68098519617818\n",
      "Train ,Epoch#18 ,batch 261/314 ,avg-loss: 39.68163149018854\n",
      "Train ,Epoch#18 ,batch 271/314 ,avg-loss: 39.70335433227989\n",
      "Train ,Epoch#18 ,batch 281/314 ,avg-loss: 39.709111739732194\n",
      "Train ,Epoch#18 ,batch 291/314 ,avg-loss: 39.72064733341387\n",
      "Train ,Epoch#18 ,batch 301/314 ,avg-loss: 39.73404636573158\n",
      "Train ,Epoch#18 ,batch 311/314 ,avg-loss: 39.740851742661654\n",
      "Train ,Epoch#18 ,loss:39.75083532181997\n",
      "Test ,batch 1/19 ,avg-loss: 38.228736877441406\n",
      "Test ,batch 11/19 ,avg-loss: 39.54670091108842\n",
      "Test ,Epoch#18 ,loss:39.253584480285646\n",
      "Train ,Epoch#19 ,batch 1/314 ,avg-loss: 38.33332061767578\n",
      "Train ,Epoch#19 ,batch 11/314 ,avg-loss: 39.62329517711293\n",
      "Train ,Epoch#19 ,batch 21/314 ,avg-loss: 39.54666010538737\n",
      "Train ,Epoch#19 ,batch 31/314 ,avg-loss: 39.677310943603516\n",
      "Train ,Epoch#19 ,batch 41/314 ,avg-loss: 39.64318187062333\n",
      "Train ,Epoch#19 ,batch 51/314 ,avg-loss: 39.66298847572476\n",
      "Train ,Epoch#19 ,batch 61/314 ,avg-loss: 39.69262576494061\n",
      "Train ,Epoch#19 ,batch 71/314 ,avg-loss: 39.61986176396759\n",
      "Train ,Epoch#19 ,batch 81/314 ,avg-loss: 39.64217263680917\n",
      "Train ,Epoch#19 ,batch 91/314 ,avg-loss: 39.69013050624302\n",
      "Train ,Epoch#19 ,batch 101/314 ,avg-loss: 39.66323705238871\n",
      "Train ,Epoch#19 ,batch 111/314 ,avg-loss: 39.630316966288795\n",
      "Train ,Epoch#19 ,batch 121/314 ,avg-loss: 39.62334933950881\n",
      "Train ,Epoch#19 ,batch 131/314 ,avg-loss: 39.63402388478053\n",
      "Train ,Epoch#19 ,batch 141/314 ,avg-loss: 39.64533328333645\n",
      "Train ,Epoch#19 ,batch 151/314 ,avg-loss: 39.6683903119422\n",
      "Train ,Epoch#19 ,batch 161/314 ,avg-loss: 39.69099812478012\n",
      "Train ,Epoch#19 ,batch 171/314 ,avg-loss: 39.677134954441364\n",
      "Train ,Epoch#19 ,batch 181/314 ,avg-loss: 39.67228721913712\n",
      "Train ,Epoch#19 ,batch 191/314 ,avg-loss: 39.675540644460945\n",
      "Train ,Epoch#19 ,batch 201/314 ,avg-loss: 39.66690224794606\n",
      "Train ,Epoch#19 ,batch 211/314 ,avg-loss: 39.65037048484477\n",
      "Train ,Epoch#19 ,batch 221/314 ,avg-loss: 39.65132218788113\n",
      "Train ,Epoch#19 ,batch 231/314 ,avg-loss: 39.647078130152316\n",
      "Train ,Epoch#19 ,batch 241/314 ,avg-loss: 39.64906868202558\n",
      "Train ,Epoch#19 ,batch 251/314 ,avg-loss: 39.65485850178388\n",
      "Train ,Epoch#19 ,batch 261/314 ,avg-loss: 39.6516877536116\n",
      "Train ,Epoch#19 ,batch 271/314 ,avg-loss: 39.648781470267096\n",
      "Train ,Epoch#19 ,batch 281/314 ,avg-loss: 39.650220239713946\n",
      "Train ,Epoch#19 ,batch 291/314 ,avg-loss: 39.63935740624916\n",
      "Train ,Epoch#19 ,batch 301/314 ,avg-loss: 39.63808073633137\n",
      "Train ,Epoch#19 ,batch 311/314 ,avg-loss: 39.63862021099716\n",
      "Train ,Epoch#19 ,loss:39.639449746268134\n",
      "Test ,batch 1/19 ,avg-loss: 39.835079193115234\n",
      "Test ,batch 11/19 ,avg-loss: 39.48114637895064\n",
      "Test ,Epoch#19 ,loss:39.39128665924072\n",
      "Train ,Epoch#20 ,batch 1/314 ,avg-loss: 39.62841033935547\n",
      "Train ,Epoch#20 ,batch 11/314 ,avg-loss: 39.12511166659269\n",
      "Train ,Epoch#20 ,batch 21/314 ,avg-loss: 39.4978993733724\n",
      "Train ,Epoch#20 ,batch 31/314 ,avg-loss: 39.58722932877079\n",
      "Train ,Epoch#20 ,batch 41/314 ,avg-loss: 39.589241958246\n",
      "Train ,Epoch#20 ,batch 51/314 ,avg-loss: 39.59171175489239\n",
      "Train ,Epoch#20 ,batch 61/314 ,avg-loss: 39.51195225950147\n",
      "Train ,Epoch#20 ,batch 71/314 ,avg-loss: 39.548523808868836\n",
      "Train ,Epoch#20 ,batch 81/314 ,avg-loss: 39.53805282969534\n",
      "Train ,Epoch#20 ,batch 91/314 ,avg-loss: 39.60728513277494\n",
      "Train ,Epoch#20 ,batch 101/314 ,avg-loss: 39.6140612989369\n",
      "Train ,Epoch#20 ,batch 111/314 ,avg-loss: 39.608385618742524\n",
      "Train ,Epoch#20 ,batch 121/314 ,avg-loss: 39.61126441009774\n",
      "Train ,Epoch#20 ,batch 131/314 ,avg-loss: 39.57204102377855\n",
      "Train ,Epoch#20 ,batch 141/314 ,avg-loss: 39.59265777912546\n",
      "Train ,Epoch#20 ,batch 151/314 ,avg-loss: 39.57614257004087\n",
      "Train ,Epoch#20 ,batch 161/314 ,avg-loss: 39.55300088254561\n",
      "Train ,Epoch#20 ,batch 171/314 ,avg-loss: 39.553295715510494\n",
      "Train ,Epoch#20 ,batch 181/314 ,avg-loss: 39.55320109583396\n",
      "Train ,Epoch#20 ,batch 191/314 ,avg-loss: 39.55723214274302\n",
      "Train ,Epoch#20 ,batch 201/314 ,avg-loss: 39.55364863077799\n",
      "Train ,Epoch#20 ,batch 211/314 ,avg-loss: 39.549351714798625\n",
      "Train ,Epoch#20 ,batch 221/314 ,avg-loss: 39.541963050807766\n",
      "Train ,Epoch#20 ,batch 231/314 ,avg-loss: 39.548060280936106\n",
      "Train ,Epoch#20 ,batch 241/314 ,avg-loss: 39.55484964837672\n",
      "Train ,Epoch#20 ,batch 251/314 ,avg-loss: 39.55493536413428\n",
      "Train ,Epoch#20 ,batch 261/314 ,avg-loss: 39.54992737167183\n",
      "Train ,Epoch#20 ,batch 271/314 ,avg-loss: 39.543069762057485\n",
      "Train ,Epoch#20 ,batch 281/314 ,avg-loss: 39.53631246980823\n",
      "Train ,Epoch#20 ,batch 291/314 ,avg-loss: 39.53154470174993\n",
      "Train ,Epoch#20 ,batch 301/314 ,avg-loss: 39.53550379300038\n",
      "Train ,Epoch#20 ,batch 311/314 ,avg-loss: 39.55106164864789\n",
      "Train ,Epoch#20 ,loss:39.5459831479996\n",
      "Test ,batch 1/19 ,avg-loss: 39.82595443725586\n",
      "Test ,batch 11/19 ,avg-loss: 39.75861705433238\n",
      "Test ,Epoch#20 ,loss:39.74100475311279\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(6,EPOCHS):\n",
    "  batches_train_losses=[]\n",
    "  for num,batch in enumerate(trainDL):\n",
    "    model.zero_grad()\n",
    "    loss=model(batch)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    batches_train_losses+=[loss.item()]\n",
    "    if num%10==0:\n",
    "      avg_loss=np.mean(batches_train_losses)\n",
    "      print('Train ,Epoch#{} ,batch {}/{} ,avg-loss: {}'.format(epoch+1,num+1,train_batch_cnt,avg_loss))\n",
    "  epoch_loss=np.mean(batches_train_losses)\n",
    "  print('Train ,Epoch#{} ,loss:{}'.format(epoch+1,epoch_loss))\n",
    "  if (epoch>0 and epoch_loss<min(epochs_train_losses)) or epoch+1==EPOCHS:\n",
    "    torch.save(model.state_dict(),'DL_FP_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss))\n",
    "    shutil.move('DL_FP_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss),'drive/My Drive/Colab Data')\n",
    "    torch.save(optimizer.state_dict(),'DL_FP_optim_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss))\n",
    "    shutil.move('DL_FP_optim_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss),'drive/My Drive/Colab Data')\n",
    "    torch.save(vgg['last'].state_dict(),'DL_FP_vgglast_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss))\n",
    "    shutil.move('DL_FP_vgglast_epoch{}_loss{:.1f}.pt'.format(epoch+1,epoch_loss),'drive/My Drive/Colab Data')\n",
    "  epochs_train_losses+=[epoch_loss]\n",
    "  #####Test\n",
    "  model.eval()\n",
    "  vggLastLayer.eval()\n",
    "  # vgg19_model.eval()\n",
    "  model.change_task('reg2cap')\n",
    "  batches_test_losses=[]\n",
    "  for num,batch in enumerate(reg2capTestDL):\n",
    "    with torch.no_grad():\n",
    "      loss,preds=model(batch)\n",
    "      batches_test_losses+=[loss.item()]\n",
    "      if num%10==0:\n",
    "        avg_loss=np.mean(batches_test_losses)\n",
    "        print('Test ,batch {}/{} ,avg-loss: {}'.format(num+1,test_batch_cnt,avg_loss))\n",
    "  model.train()\n",
    "  vggLastLayer.train()\n",
    "  # vgg19_model.train()\n",
    "  epoch_loss=np.mean(batches_test_losses)\n",
    "  epochs_test_losses+=[epoch_loss]\n",
    "  print('Test ,Epoch#{} ,loss:{}'.format(epoch+1,epoch_loss))\n",
    "  f=open('drive/My Drive/Colab Data/loss_history.json','w')\n",
    "  s=json.dumps({'train':epochs_train_losses,'test':epochs_test_losses})\n",
    "  f.write(s)\n",
    "  f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10502132,
     "status": "ok",
     "timestamp": 1597123370957,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "r_r6oC62MDPs",
    "outputId": "a9b477be-ca75-431f-ecd9-1fb0f232ba09"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs_train_losses)\n",
    "plt.plot(epochs_test_losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train','Test'],loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H78T-ab9Xckn"
   },
   "source": [
    "# 5.Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TWirQZ9k8Wbs"
   },
   "outputs": [],
   "source": [
    "vocab=Vocab.load('drive/My Drive/Colab Data/DL_FP_vocab.vo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 58857,
     "status": "ok",
     "timestamp": 1596217433350,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
      "userId": "07513140462530453530"
     },
     "user_tz": -270
    },
    "id": "WyNhiK4-8XUM",
    "outputId": "edbc70e2-f47f-4cb9-f3eb-e490aeac2ddb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing 256 samples finished!\n",
      "preparing 512 samples finished!\n",
      "preparing 768 samples finished!\n",
      "preparing 1024 samples finished!\n",
      "preparing 1280 samples finished!\n",
      "preparing 1536 samples finished!\n",
      "preparing 1792 samples finished!\n",
      "preparing 2048 samples finished!\n",
      "preparing 2304 samples finished!\n",
      "preparing 2560 samples finished!\n",
      "preparing 2816 samples finished!\n",
      "preparing 3072 samples finished!\n",
      "preparing 3328 samples finished!\n",
      "preparing 3584 samples finished!\n",
      "preparing 3840 samples finished!\n",
      "preparing 4096 samples finished!\n",
      "preparing 4352 samples finished!\n",
      "preparing 4608 samples finished!\n",
      "preparing 4864 samples finished!\n",
      "preparing 4895 samples finished!\n",
      "Reading Dataset file finished!\n"
     ]
    }
   ],
   "source": [
    "validDS=CapRegDataset(vgg,mode='valid',vocab=vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1357,
     "status": "ok",
     "timestamp": 1596217883980,
     "user": {
      "displayName": "sadra barikbin",
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    "id": "popcsQi6J9og",
    "outputId": "d6364009-e49a-4ecb-94cf-d61f0658519b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<All keys matched successfully>\n"
     ]
    }
   ],
   "source": [
    "model=CapRegModel(vocab.size).to(DEVICE)\n",
    "print(model.load_state_dict(torch.load('drive/My Drive/Colab Data/DL_FP_epoch15_loss50.752.pt')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j-Pexbt1IMlT"
   },
   "outputs": [],
   "source": [
    "reg2capTestDL=DataLoader(validDS,batch_size=BATCH_SIZE,shuffle=True,num_workers=0,collate_fn=BatchPrepareCollate(vgg,fcnn,mode='test'))\n",
    "cap2regTestDL=DataLoader(validDS,batch_size=8,shuffle=True,num_workers=0,collate_fn=BatchPrepareCollate(vgg,fcnn,mode='test',task='cap2reg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 45831,
     "status": "ok",
     "timestamp": 1596217844239,
     "user": {
      "displayName": "sadra barikbin",
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    },
    "id": "ZkgHHvD11-h8",
    "outputId": "ebc7fa7f-1421-48a7-ba34-b7dd6e8b34f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test ,batch 1/38\n",
      "Test ,batch 11/38\n",
      "Test ,batch 21/38\n",
      "Test ,batch 31/38\n"
     ]
    }
   ],
   "source": [
    "test_batch_cnt=len(validDS)//BATCH_SIZE\n",
    "model.eval()\n",
    "vgg['last'].eval()\n",
    "model.change_task('reg2cap')\n",
    "cap_predictions=[]\n",
    "for num,batch in enumerate(reg2capTestDL):\n",
    "    with torch.no_grad():\n",
    "      preds=model.generate_caption(batch)\n",
    "    for i in range(len(preds)):\n",
    "      preds[i]=' '.join([vocab.get_token_by_id(id) for id in preds[i]])\n",
    "    cap_predictions+=preds\n",
    "    if num%10==0:\n",
    "      print('Test ,batch {}/{}'.format(num+1,test_batch_cnt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7547,
     "status": "ok",
     "timestamp": 1596217851797,
     "user": {
      "displayName": "sadra barikbin",
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    },
    "id": "99m2rZIiXitR",
    "outputId": "4e874bb2-8eac-4bed-fc82-0dcb77a77946"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0768187242820195\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "tgt_frame=pd.read_csv('dataset.valid.csv')\n",
    "from bleu_score import eval_bleu\n",
    "print(eval_bleu(tgt_frame,cap_predictions,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 764740,
     "status": "ok",
     "timestamp": 1596218651711,
     "user": {
      "displayName": "sadra barikbin",
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     },
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    },
    "id": "84-Q9AOjBqG5",
    "outputId": "fc3b3f60-0697-480f-a8c4-5078d2350e2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test ,batch 1/611\n",
      "Test ,batch 11/611\n",
      "Test ,batch 21/611\n",
      "Test ,batch 31/611\n",
      "Test ,batch 41/611\n",
      "Test ,batch 51/611\n",
      "Test ,batch 61/611\n",
      "Test ,batch 71/611\n",
      "Test ,batch 81/611\n",
      "Test ,batch 91/611\n",
      "Test ,batch 101/611\n",
      "Test ,batch 111/611\n",
      "Test ,batch 121/611\n",
      "Test ,batch 131/611\n",
      "Test ,batch 141/611\n",
      "Test ,batch 151/611\n",
      "Test ,batch 161/611\n",
      "Test ,batch 171/611\n",
      "Test ,batch 181/611\n",
      "Test ,batch 191/611\n",
      "Test ,batch 201/611\n",
      "Test ,batch 211/611\n",
      "Test ,batch 221/611\n",
      "Test ,batch 231/611\n",
      "Test ,batch 241/611\n",
      "Test ,batch 251/611\n",
      "Test ,batch 261/611\n",
      "Test ,batch 271/611\n",
      "Test ,batch 281/611\n",
      "Test ,batch 291/611\n",
      "Test ,batch 301/611\n",
      "Test ,batch 311/611\n",
      "Test ,batch 321/611\n",
      "Test ,batch 331/611\n",
      "Test ,batch 341/611\n",
      "Test ,batch 351/611\n",
      "Test ,batch 361/611\n",
      "Test ,batch 371/611\n",
      "Test ,batch 381/611\n",
      "Test ,batch 391/611\n",
      "Test ,batch 401/611\n",
      "Test ,batch 411/611\n",
      "Test ,batch 421/611\n",
      "Test ,batch 431/611\n",
      "Test ,batch 441/611\n",
      "Test ,batch 451/611\n",
      "Test ,batch 461/611\n",
      "Test ,batch 471/611\n",
      "Test ,batch 481/611\n",
      "Test ,batch 491/611\n",
      "Test ,batch 501/611\n",
      "Test ,batch 511/611\n",
      "Test ,batch 521/611\n",
      "Test ,batch 531/611\n",
      "Test ,batch 541/611\n",
      "Test ,batch 551/611\n",
      "Test ,batch 561/611\n",
      "Test ,batch 571/611\n",
      "Test ,batch 581/611\n",
      "Test ,batch 591/611\n",
      "Test ,batch 601/611\n",
      "Test ,batch 611/611\n"
     ]
    }
   ],
   "source": [
    "test_batch_cnt=len(validDS)//8\n",
    "model.eval()\n",
    "vgg['last'].eval()\n",
    "model.change_task('cap2reg')\n",
    "box_predictions=[]\n",
    "for num,batch in enumerate(cap2regTestDL):\n",
    "    with torch.no_grad():\n",
    "      preds=model(batch)\n",
    "    box_predictions+=[tuple(pred[0][:4].cpu().numpy()) for pred in preds]\n",
    "    if num%10==0:\n",
    "      print('Test ,batch {}/{}'.format(num+1,test_batch_cnt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1235,
     "status": "ok",
     "timestamp": 1596219184761,
     "user": {
      "displayName": "sadra barikbin",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiCq0YqNHWfcZyly3I_AMTkvS8_tO8PZOQpGtkK=s64",
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    },
    "id": "zQyqpe1HEg2Y",
    "outputId": "93230247-3723-413c-de6e-982ed2e9b3f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.050459652706843716, 0.13871651949272282)\n"
     ]
    }
   ],
   "source": [
    "from comprehension_score import eval_precision\n",
    "print(eval_precision(tgt_frame,box_predictions))"
   ]
  }
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