sadimanna

@echo off 
setlocal enabledelayedexpansion 
 
REM Set the paths for Folder A, train folder, val folder, and test folder 
set "base_path=C:\Users\ISI_UTS\Siladittya\MIDA2023" 
set "image_folder_path=%base_path%\images" 
set "train_image_path=%base_path%\train_images" 
set "val_image_path=%base_path%\val_images" 
set "test_image_path=%base_path%\test_images" 
 

sadimanna

#!/bin/bash

# Set the paths for the train folder, label folder, and target folder
train_folder_path="/path/to/train"
label_folder_path="/path/to/label"
target_folder_path="/path/to/target"
label_ext = ".labext"

# Create the target folder if it doesn't exist
mkdir -p "$target_folder_path"

sadimanna

#!/bin/bash

# Set the paths for Folder A and the three target folders
folder_a_path="/path/to/FolderA"
train_folder_path="/path/to/train"
val_folder_path="/path/to/val"
test_folder_path="/path/to/test"

# Create the target folders if they don't exist
mkdir -p "$train_folder_path"

sadimanna

#!/bin/bash

# Set the paths for the train folder, label folder, and target folder
image_folder_path="/path/to/train"
label_folder_path="/path/to/label"
target_train_folder="/path/to/train_split"
target_val_folder="/path/to/val_split"
target_test_folder="/path/to/test_split"

# Create the target folders if they don't exist

sadimanna

==========================================================================================================================================================================
Layer (type:depth-idx)                        Input Shape               Output Shape              Param #                   Kernel Shape              Mult-Adds
==========================================================================================================================================================================
Model                                         [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
├─ResNet: 1-1                                 [1, 3, 64, 64]            [1, 1000]                 --                        --                        --
│    └─Conv2d: 2-1                            [1, 3, 64, 64]            [1, 64, 32, 32]           9,408                     [7, 7]                    9,633,792
│    └─BatchNorm2d: 2-2                       [1, 64, 32

sadimanna

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1           [-1, 64, 32, 32]           9,408
       BatchNorm2d-2           [-1, 64, 32, 32]             128
              ReLU-3           [-1, 64, 32, 32]               0
         MaxPool2d-4           [-1, 64, 16, 16]               0
.
.
           Linear-68                 [-1, 1000]         513,000

sadimanna

=====================================================================================================================================================================
Layer (type:depth-idx)                   Input Shape               Output Shape              Param #                   Kernel Shape              Mult-Adds
=====================================================================================================================================================================
ResNet                                   [1, 3, 224, 224]          [1, 1000]                 --                        --                        --
├─Conv2d: 1-1                            [1, 3, 224, 224]          [1, 64, 112, 112]         9,408                     [7, 7]                    118,013,952
├─BatchNorm2d: 1-2                       [1, 64, 112, 112]         [1, 64, 112, 112]         128                       --                        128
├─ReLU: 1-3                              [1, 64, 112, 112]         [1, 64, 112, 112

sadimanna

!pip uninstall -y torch
!pip install torch==1.8.2+cpu torchvision==0.9.2+cpu -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

sadimanna

class ArrayDataset:
    def __init__(self, 
                 phase, 
                 array, 
                 labels, 
                 mean, std, 
                 transformations = None):
        self.phase = phase
        self.imgarr = copy.deepcopy(array)
        self.labels = copy.deepcopy(labels)

sadimanna

class GradCamModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.gradients = None
        self.tensorhook = []
        self.layerhook = []
        self.selected_out = None
        
        #PRETRAINED MODEL
        self.pretrained = models.resnet50(pretrained=True)