add incomplete work related to plates leak

an eval-20 run was also performed for the efficientnet-3 w/ 2 sites 512px w/ 448px crops

eval10_experiment5.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# ## Load libraries
+
+# In[1]:
+
+
+get_ipython().system('pip install -q -r requirements.txt')
+
+
+# In[1]:
+
+
+import sys
+import os
+import numpy as np
+import pandas as pd
+
+from PIL import Image
+
+import torch
+import torch.nn as nn
+import torch.utils.data as D
+from torch.optim.lr_scheduler import ExponentialLR
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+from torchvision import transforms
+
+from ignite.engine import Events
+from scripts.ignite import create_supervised_evaluator, create_supervised_trainer
+from ignite.metrics import Loss, Accuracy
+from ignite.contrib.handlers.tqdm_logger import ProgressBar
+from ignite.handlers import  EarlyStopping, ModelCheckpoint
+from ignite.contrib.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler
+
+from tqdm import tqdm_notebook
+
+from sklearn.model_selection import train_test_split
+
+from efficientnet_pytorch import EfficientNet
+
+from scripts.evaluate import eval_model, eval_model_10
+
+import warnings
+warnings.filterwarnings('ignore')
+
+
+# ## Define dataset and model
+
+# In[2]:
+
+
+img_dir = '../input/rxrxairgb512'
+path_data = '../input/rxrxaicsv'
+device = 'cuda'
+batch_size = 32
+torch.manual_seed(0)
+model_name = 'efficientnet-b3'
+
+
+# In[3]:
+
+
+jitter = (0.6, 1.4)
+class ImagesDS(D.Dataset):
+    # taken textbook from https://arxiv.org/pdf/1812.01187.pdf
+    transform_train = transforms.Compose([
+        transforms.RandomResizedCrop(448),
+        transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1),
+        transforms.RandomHorizontalFlip(p=0.5),
+        # PCA Noise should go here,
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))
+    ])
+
+    transform_validation = transforms.Compose([
+        transforms.CenterCrop(448),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))
+    ])
+
+    def __init__(self, df, img_dir=img_dir, mode='train', validation=False, site=1):
+        self.records = df.to_records(index=False)
+        self.site = site
+        self.mode = mode
+        self.img_dir = img_dir
+        self.len = df.shape[0]
+        self.validation = validation
+
+    @staticmethod
+    def _load_img_as_tensor(file_name, validation):
+        with Image.open(file_name) as img:
+            if not validation:
+                return ImagesDS.transform_train(img)
+            else:
+                return ImagesDS.transform_validation(img)
+
+    def _get_img_path(self, index, site=1):
+        experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate
+        return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg'
+
+    def __getitem__(self, index):
+        img1, img2 = [self._load_img_as_tensor(self._get_img_path(index, site), self.validation) for site in [1,2]]
+        if self.mode == 'train':
+            return img1, img2, int(self.records[index].sirna)
+        else:
+            return img1, img2, self.records[index].id_code
+
+    def __len__(self):
+        return self.len
+
+
+class TestImagesDS(D.Dataset):
+    transform = transforms.Compose([
+        transforms.RandomCrop(448),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))
+    ])
+
+    def __init__(self, df, img_dir=img_dir, mode='test', validation=False, site=1):
+        self.records = df.to_records(index=False)
+        self.site = site
+        self.mode = mode
+        self.img_dir = img_dir
+        self.len = df.shape[0]
+        self.validation = validation
+
+    @staticmethod
+    def _load_img_as_tensor(file_name):
+        with Image.open(file_name) as img:
+            return TestImagesDS.transform(img)
+
+    def _get_img_path(self, index, site=1):
+        experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate
+        return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg'
+
+    def get_image_pair(self, index):
+        return [self._load_img_as_tensor(self._get_img_path(index, site)) for site in [1,2]]
+
+    def __getitem__(self, index):
+        image_pairs = [self.get_image_pair(index) for _ in range(20)]
+
+        return image_pairs, self.records[index].id_code
+
+    def __len__(self):
+        return self.len
+
+
+# In[4]:
+
+
+# dataframes for training, cross-validation, and testing
+df_test = pd.read_csv(path_data+'/test.csv')
+
+# pytorch test dataset & loader
+ds_test = TestImagesDS(df_test, mode='test', validation=True)
+tloader = D.DataLoader(ds_test, batch_size=1, shuffle=False, num_workers=4)
+
+
+# In[5]:
+
+
+class EfficientNetTwoInputs(nn.Module):
+    def __init__(self):
+        super(EfficientNetTwoInputs, self).__init__()
+        self.classes = 1108
+
+        model = model = EfficientNet.from_pretrained(model_name, num_classes=1108) 
+        num_ftrs = model._fc.in_features
+        model._fc = nn.Identity()
+
+        self.resnet = model
+        self.fc = nn.Linear(num_ftrs * 2, self.classes)
+
+    def forward(self, x1, x2):
+        x1_out = self.resnet(x1)
+        x2_out = self.resnet(x2)
+
+        N, _, _, _ = x1.size()
+        x1_out = x1_out.view(N, -1)
+        x2_out = x2_out.view(N, -1)
+
+        out = torch.cat((x1_out, x2_out), 1)
+        out = self.fc(out)
+
+        return out 
+
+model = EfficientNetTwoInputs()
+
+
+# #### Evaluate
+
+# In[6]:
+
+
+model.cuda()
+eval_model_10(model, tloader, 'models/Model_efficientnet-b3_93.pth', path_data)
+