First commit

.gitignore

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+# Created by .ignore support plugin (hsz.mobi)
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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
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+# virtualenv
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+# Rope project settings
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+### VirtualEnv template
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+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
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+# idea folder, uncomment if you don't need it
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.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/.name

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+2D_Data_Generator_Model.py

.idea/2D_Data_Generator.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Transformer_Testing)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/2D_Data_Generator.iml" filepath="$PROJECT_DIR$/.idea/2D_Data_Generator.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

2D_Data_Generator_Model.py

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+import random
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from datasets import load_dataset, ClassLabel, Sequence
+import json
+import numpy
+from transformers import AutoImageProcessor
+from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
+from transformers import DefaultDataCollator
+# import evaluate
+import numpy as np
+from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
+from PIL import Image
+from matplotlib import cm
+from Data_Generation.Shape_Generation_Functions import basic_box, diagonal_box_split, horizontal_vertical_box_split, \
+    back_slash_box, forward_slash_box, back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, \
+    x_hamburger_box, x_hot_dog_box, x_plus_box
+
+from Data_Generation.Dataset_Generation_Functions import make_boxes
+
+
+
+# food = load_dataset("cmudrc/2d-lattices", split="train[:15]")  # Loads the training data samples
+food = load_dataset("cmudrc/2d-lattices", split="train+test")  # Loads all of the data, for use after training
+
+# checks to see if the dataset has been assigned a class label
+# if type(food.features["label"]) != 'datasets.features.features.ClassLabel': # Cast to ClassLabel
+#     food = food.class_encode_column('label')
+print(food)
+desired_label = 'x_hot_dog_box'
+desired_thickness = 1
+desired_density = 1
+
+data_frame = pd.DataFrame(food)
+# print(data_frame)
+
+shape_rows = data_frame['Shape'] == desired_label
+# print(shape_rows)
+
+thickness_rows = data_frame['Thickness'] == desired_thickness
+# print(thickness_rows)
+
+density_rows = data_frame['Density'] == desired_density
+# print(density_rows)
+
+desired_output = data_frame.loc[shape_rows & thickness_rows & density_rows].iloc[0]['Array']
+print(desired_output)
+print(type(desired_output))
+
+
+example_point = numpy.array(json.loads(desired_output))
+
+plt.imshow(example_point)
+plt.show()
+
+
+all_shapes = [basic_box, diagonal_box_split, horizontal_vertical_box_split, back_slash_box, forward_slash_box,
+                 back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, x_hamburger_box,
+                 x_hot_dog_box, x_plus_box]
+
+base_shapes = [basic_box, back_slash_box, forward_slash_box, hot_dog_box, hamburger_box]
+image_size = 11
+density = [1]
+
+boxes = make_boxes(image_size, density, all_shapes)
+
+
+box_arrays, box_shape, box_density, box_thickness,  = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3]
+
+# indices_1 = [i for i in range(len(boxes)) if boxes[1][i] == str(base_shapes[0]) and boxes[2][i] == density[0] and boxes[3][i] == desired_thickness]
+indices_1 = [i for i in range(len(box_arrays)) if box_shape[i] == desired_label and box_density[i] == desired_density and box_thickness[i] == desired_thickness]
+print(indices_1)
+# indices_1 = random.randint(0, len(box_arrays))
+
+
+# plt.imshow(box_arrays[indices_1])
+plt.imshow(box_arrays[indices_1[0]])
+plt.show()
+
+
+'''trainer.push_to_hub()''' # Need to figure out how to push the model to the hub

Data_Generation/Dataset_Generation_Functions.py

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+import numpy as np
+
+
+########################################################################################################################
+# Make the data using all the code in Shape_Generation_Functions.py
+def make_boxes(image_size, densities, shapes):
+    """
+    :param image_size: [int] - the pixel height and width of the generated arrays
+    :param densities: [list] - of the values of each of the active pixels in each shape
+    :param shapes: [list] - of the various shapes desired for the dataset
+    :return: [list[tuple]] - [Array, Density, Thickness, Shape]
+    """
+
+    matrix = []
+
+    for function in shapes:  # Adds different types of shapes
+
+        # Adds different density values
+        for i in range(len(densities)):
+            # Loops through the possible thickness values
+            for j in range(image_size):  # Adds additional Pixels
+                thickness = j
+                Array = (function(thickness, densities[i], image_size))
+
+                # Checks if there are any 0's left in the array to append
+                if (np.where((Array == float(0)))[0] > 0).any():
+                    the_tuple = (Array, str(function.__name__), densities[i], thickness)
+                    matrix.append(the_tuple)
+
+                # Prevents solids shapes from being appended to the array
+                else:
+                    break
+    return matrix
+
+
+########################################################################################################################
+
+

Data_Generation/Piecewise_Box_Functions.py

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+import numpy as np
+import math
+
+
+def basic_box_array(image_size):
+    A = np.ones((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    # Creates the outside edges of the box
+    # for i in range(image_size):
+    #     for j in range(image_size):
+    #         if i == 0 or j == 0 or i == image_size - 1 or j == image_size - 1:
+    #             A[i][j] = 1
+    # A[1:-1, 1:-1] = 1
+    # np.pad(A[1:-1,1:-1], pad_width=((1, 1), (1, 1)), mode='constant', constant_values=1)
+    A[1:-1, 1:-1] = 0
+    return A
+
+
+def back_slash_array(image_size):
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    # for i in range(image_size):
+    #     for j in range(image_size):
+    #         if i == j:
+    #             A[i][j] = 1
+    np.fill_diagonal(A, 1)
+
+    return A
+
+
+def forward_slash_array(image_size):
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    # for i in range(image_size):
+    #     for j in range(image_size):
+    #         if i == (image_size-1)-j:
+    #             A[i][j] = 1
+    np.fill_diagonal(np.fliplr(A), 1)
+    return A
+
+
+def hot_dog_array(image_size):
+    # Places pixels down the vertical axis to split the box
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    # for i in range(image_size):
+    #     for j in range(image_size):
+    #         if j == math.floor((image_size - 1) / 2) or j == math.ceil((image_size - 1) / 2):
+    #             A[i][j] = 1
+
+    A[:, np.floor((image_size - 1) / 2).astype(int)] = 1
+    A[:, np.ceil((image_size - 1) / 2).astype(int)] = 1
+    return A
+
+
+def hamburger_array(image_size):
+    # Places pixels across the horizontal axis to split the box
+    A = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    # for i in range(image_size):
+    #     for j in range(image_size):
+    #         if i == math.floor((image_size - 1) / 2) or i == math.ceil((image_size - 1) / 2):
+    #             A[i][j] = 1
+    A[np.floor((image_size - 1) / 2).astype(int), :] = 1
+    A[np.ceil((image_size - 1) / 2).astype(int), :] = 1
+    return A
+
+
+# def update_array(array_original, array_new, image_size):
+#     A = array_original
+#     for i in range(image_size):
+#         for j in range(image_size):
+#             if array_new[i][j] == 1:
+#                 A[i][j] = 1
+#     return A
+def update_array(array_original, array_new, image_size):
+    A = array_original
+    A[array_new == 1] = 1
+    return A
+
+
+def add_pixels(array_original, additional_pixels, image_size):
+    # Adds pixels to the thickness of each component of the box
+    A = array_original
+    A_updated = np.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
+    for dens in range(additional_pixels):
+        for i in range(1, image_size - 1):
+            for j in range(1, image_size - 1):
+                if A[i - 1][j] + A[i + 1][j] + A[i][j - 1] + A[i][j + 1] > 0:
+                    A_updated[i][j] = 1
+        A = update_array(A, A_updated,image_size)
+    return A
+

Data_Generation/Shape_Generation_Functions.py

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+from Data_Generation.Piecewise_Box_Functions import back_slash_array, basic_box_array, forward_slash_array, \
+    hot_dog_array, hamburger_array, update_array, add_pixels
+
+
+########################################################################################################################
+# Series of Basic Box Shapes
+
+def basic_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Creates the outside edges of the box
+    # Increase the thickness of each part of the box
+    A = add_pixels(A, additional_pixels, image_size)
+    return A*density
+
+
+def horizontal_vertical_box_split(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Creates the outside edges of the box
+    # Place pixels across the horizontal and vertical axes to split the box
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = update_array(A, hamburger_array(image_size), image_size)
+    # Increase the thickness of each part of the box
+    A = add_pixels(A, additional_pixels, image_size)
+    return A*density
+
+
+def diagonal_box_split(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Creates the outside edges of the box
+
+    # Add pixels along the diagonals of the box
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = update_array(A, forward_slash_array(image_size), image_size)
+
+    # Adds pixels to the thickness of each component of the box
+    # Increase the thickness of each part of the box
+    A = add_pixels(A, additional_pixels, image_size)
+    return A*density
+
+
+def back_slash_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def forward_slash_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, forward_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def hot_dog_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def hamburger_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hamburger_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def x_plus_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = update_array(A, hamburger_array(image_size), image_size)
+    A = update_array(A, forward_slash_array(image_size), image_size)
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def forward_slash_plus_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = update_array(A, hamburger_array(image_size), image_size)
+    A = update_array(A, forward_slash_array(image_size), image_size)
+    # A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def back_slash_plus_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = update_array(A, hamburger_array(image_size), image_size)
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def x_hot_dog_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hot_dog_array(image_size), image_size)
+    A = update_array(A, forward_slash_array(image_size), image_size)
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+
+
+def x_hamburger_box(additional_pixels, density, image_size):
+    A = basic_box_array(image_size)  # Initializes A matrix with 0 values
+    A = update_array(A, hamburger_array(image_size), image_size)
+    A = update_array(A, forward_slash_array(image_size), image_size)
+    A = update_array(A, back_slash_array(image_size), image_size)
+    A = add_pixels(A, additional_pixels, image_size)
+    return A * density
+