update app.py

app.py

@@ -1,13 +1,8 @@
 # gradioMisClassGradCAMimageInputter
 import os
-import math
-import numpy as np
-import pandas as pd
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
 import torchvision
-import matplotlib.pyplot as plt
+from torchvision import datasets, transforms, utils
 from pl_bolts.datamodules import CIFAR10DataModule
 from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
 from pytorch_lightning import LightningModule, Trainer, seed_everything
@@ -17,40 +12,71 @@ from pytorch_lightning.loggers import CSVLogger
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.optim.swa_utils import AveragedModel, update_bn
 from torchmetrics.functional import accuracy
-from pytorch_lightning.callbacks import ModelCheckpoint
-from torchvision import datasets, transforms, utils
-from PIL import Image
-from pytorch_grad_cam import GradCAM
-from pytorch_grad_cam.utils.image import show_cam_on_image
-import gradio as gr
+import pandas as pd
+import seaborn as sn
+import torch.nn as nn
+import torch.nn.functional as F
+# from IPython.core.display import display
 import misclas_helper
 import gradcam_helper
 import lightningmodel
+import trainsave_loadtest
 from misclas_helper import display_cifar_misclassified_data
 from gradcam_helper import display_gradcam_output
 from misclas_helper import get_misclassified_data2
+from misclas_helper import classify_images
 from lightningmodel import LitResnet
+from trainsave_loadtest import ts_lt
+import numpy as np
+import gradio as gr
+from PIL import Image
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from torchvision import datasets, transforms, utils
 
-fileName = None
+save1_or_load0 = False
+
+!mkdir -p /content # This path will be used for saving the weights file after training
 
+!git clone https://github.com/rajayourfriend/EraV2/
+!cp EraV2/S14/*.py .
+!cp EraV2/S14/*.jpg .
+if save1_or_load0 == False:
+  !cp /content/EraV2/S14/weights_92.ckpt /content/weights.ckpt
+wt_fname = "/content/weights.ckpt" # weights file name to load
+
+model, trainer = ts_lt(save1_or_load0, Epochs = 26, wt_fname = "/content/weights.ckpt") # Train and Save Vs Load and Test
+'''
+ts_lt(save1_or_load0, # decision maker for training Vs testing
+          Epochs = 1, # argument for training
+          wt_fname = "/content/weights.ckpt" # argument for testing
+          )
+'''
 targets = None
 device = torch.device("cpu")
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-model = LitResnet(lr=0.05).load_from_checkpoint("weights_92.ckpt")
 
 device = torch.device("cpu")
 
-# Denormalize the data using test mean and std deviation
+# Get the misclassified data from test dataset
+misclassified_data = misclas_helper.get_misclassified_data2(model, device, 20)
+
+
+
+################################################################################################
+
+
+
+
+fileName = None
+
 inv_normalize = transforms.Normalize(
-    mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
-    std=[1/0.23, 1/0.23, 1/0.23]
+  mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
+  std=[1/0.23, 1/0.23, 1/0.23]
 )
 
-# Get the misclassified data from test dataset
-misclassified_data = get_misclassified_data2(model, device, 20)
-
 def hello(DoYouWantToShowMisClassifiedImages, HowManyImages):
   if(DoYouWantToShowMisClassifiedImages.lower() == "yes"):
     fileName = misclas_helper.display_cifar_misclassified_data(misclassified_data, classes, inv_normalize, number_of_samples=HowManyImages)
@@ -72,7 +98,7 @@ misClass_demo = gr.Interface(
 targets = None
 device = torch.device("cpu")
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
-           'dog', 'frog', 'horse', 'ship', 'truck')
+           'dog', 'frog', 'horse', 'ship', 'truck', 'No Image')
 
 
 def inference(DoYouWantToShowGradCAMMedImages, HowManyImages, WhichLayer, transparency):
@@ -89,9 +115,9 @@ def inference(DoYouWantToShowGradCAMMedImages, HowManyImages, WhichLayer, transp
 gradCAM_demo = gr.Interface(
     fn=inference,
         #DoYouWantToShowGradCAMMedImages, HowManyImages, WhichLayer, transparency
-    inputs=[ gr.Textbox(label="Do you want to show GradCammed images ?", placeholder="Yes / yes / YES", lines=1), 
-             gr.Slider(0, 20, step=5, label = "How many images ?"), 
-             gr.Slider(-3, -1, value = -1, step=1, label = "Which layer ?"), 
+    inputs=[ gr.Textbox(label="Do you want to show gradCammed images ?", placeholder="Yes / yes / YES", lines=1),
+             gr.Slider(0, 20, step=5, label = "How many images ?"),
+             gr.Slider(-3, -1, value = -1, step=1, label = "Which layer ?"),
              gr.Slider(0, 1, value = 0.7, label = "Overall Opacity of the Overlay")],
     outputs=['image'],
     title="GradCammed Images",
@@ -101,8 +127,15 @@ gradCAM_demo = gr.Interface(
 
 ############
 
-def ImageInputter(img1, img2, img3, img4, img5, img6, img7, img8, img9, img10):
-  return img1, img2, img3, img4, img5, img6, img7, img8, img9, img10
+def ImageInputter(img0, img1, img2, img3, img4, img5, img6, img7, img8, img9):
+  list_images = [img0, img1, img2, img3, img4, img5, img6, img7, img8, img9]
+  classified_data = classify_images(list_images, model.model, device)
+  img_out = []
+  pred_out = []
+  for img, pred in classified_data:
+    img_out.append(img)
+    pred_out.append(pred)
+  return classes[pred_out[0]], img_out[0], classes[pred_out[1]], img_out[1], classes[pred_out[2]], img_out[2], classes[pred_out[3]], img_out[3], classes[pred_out[4]], img_out[4], classes[pred_out[5]], img_out[5], classes[pred_out[6]], img_out[6], classes[pred_out[7]], img_out[7], classes[pred_out[8]], img_out[8], classes[pred_out[9]], img_out[9]
 
 imageInputter_demo = gr.Interface(
     ImageInputter,
@@ -110,25 +143,23 @@ imageInputter_demo = gr.Interface(
         "image","image","image","image","image","image","image","image","image","image"
     ],
     [
-        gr.Image("image", label = "output 1"),
-        gr.Image("image", label = "output 2"),
-        gr.Image("image", label = "output 3"),
-        gr.Image("image", label = "output 4"),
-        gr.Image("image", label = "output 5"),
-        gr.Image("image", label = "output 6"),
-        gr.Image("image", label = "output 7"),
-        gr.Image("image", label = "output 8"),
-        gr.Image("image", label = "output 9"),
-        gr.Image("image", label = "output 10")
+        gr.Textbox("text", label = "pred 0"), gr.Image("image", label = "img 0"),
+        gr.Textbox("text", label = "pred 1"), gr.Image("image", label = "img 1"),
+        gr.Textbox("text", label = "pred 2"), gr.Image("image", label = "img 2"),
+        gr.Textbox("text", label = "pred 3"), gr.Image("image", label = "img 3"),
+        gr.Textbox("text", label = "pred 4"), gr.Image("image", label = "img 4"),
+        gr.Textbox("text", label = "pred 5"), gr.Image("image", label = "img 5"),
+        gr.Textbox("text", label = "pred 6"), gr.Image("image", label = "img 6"),
+        gr.Textbox("text", label = "pred 7"), gr.Image("image", label = "img 7"),
+        gr.Textbox("text", label = "pred 8"), gr.Image("image", label = "img 8"),
+        gr.Textbox("text", label = "pred 9"), gr.Image("image", label = "img 9")
     ],
     examples=[
-        ["bird.jpg", "car.jpg", "cat.jpg"],
-        ["deer.jpg", "dog.jpg", "frog.jpg"],
-        ["horse.jpg", "plane.jpg", "ship.jpg"],
-        [None, "truck.jpg", None],
+        ["bird.jpg", "car.jpg", "cat.jpg", "deer.jpg", "dog.jpg", "frog.jpg", "horse.jpg", "plane.jpg", "ship.jpg"],
+        [None, None, None, None, "truck.jpg", None, None, None, None],
     ],
-    title="Max 10 images input",
-    description="Here's a sample image inputter. Allows you to feed in 10 images and display them. You may drag and drop images from bottom examples to the input feeders",
+    title="Max 10 images input Classifier",
+    description="Here's a sample image inputter. Allows you to feed in 10 images and display them with classification result. You may drag and drop images from bottom examples to the input feeders. You may copy and paste the images from examples to the input feeders. You may double click on a row of images from examples to get them filled in input feeders.",
 )