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Mouhamadou #1

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d7b4703
notebook with attention rollout working
momoth12 Mar 6, 2024
b870a30
reproducing images
nicolassinott Mar 6, 2024
72365a6
notebook with attention rollout working
momoth12 Mar 6, 2024
33f3806
test_on_images
momoth12 Mar 6, 2024
4fdf6e2
Merge branch 'Nicolas' of https://github.com/momoth12/Project-Explain…
momoth12 Mar 6, 2024
5e384cd
add getting class
nicolassinott Mar 6, 2024
fd567cd
test_on_images
momoth12 Mar 6, 2024
a09aa41
adding notebook for understanding model structure
nicolassinott Mar 9, 2024
1281816
cleaning
momoth12 Mar 9, 2024
024ee76
Merge branch 'Nicolas' of https://github.com/momoth12/Project-Explain…
momoth12 Mar 9, 2024
e94a0bd
updating model_structure notebook
nicolassinott Mar 9, 2024
cafcb26
adding rollout notebook that aims to explore the attention rollout fu…
nicolassinott Mar 9, 2024
323cebf
adding code for helping understand rollout and raw attention
nicolassinott Mar 9, 2024
9836014
Merge branch 'Nicolas' of https://github.com/momoth12/Project-Explain…
momoth12 Mar 9, 2024
f071a84
updating notebook
nicolassinott Mar 9, 2024
53d2103
initializing the class for Attention flow algorithm
nicolassinott Mar 9, 2024
4df5493
adding notebook for exploring graph package
nicolassinott Mar 9, 2024
6daba0b
adding implementation of flow (with bugs)
nicolassinott Mar 10, 2024
b4b98a7
steps for implementing attention flow
nicolassinott Mar 10, 2024
a551a90
adding attention flow implementation
nicolassinott Mar 10, 2024
3d94207
adding small commenting for possible bug
nicolassinott Mar 10, 2024
44cfc62
Merge branch 'Nicolas' of https://github.com/momoth12/Project-Explain…
momoth12 Mar 10, 2024
a30ffc8
correcting bugs
nicolassinott Mar 10, 2024
1ec6dd8
reproducing images
nicolassinott Mar 6, 2024
6702012
adding implementation of attention flow and notebook for visualization
nicolassinott Mar 10, 2024
290fdfa
performing fgsm attack on att_flow
momoth12 Mar 10, 2024
7855863
clearing
momoth12 Mar 10, 2024
5bb1278
adding notebook for blank out
nicolassinott Mar 10, 2024
ef421dd
clearing
momoth12 Mar 11, 2024
2cf0874
clearing
momoth12 Mar 11, 2024
ea613f8
Merge branch 'Nicolas' of https://github.com/momoth12/Project-Explain…
momoth12 Mar 11, 2024
2b7abed
adding images for study
nicolassinott Mar 11, 2024
61a804b
adding the pipeline for evaluation
nicolassinott Mar 11, 2024
cd4a468
started attacks
momoth12 Mar 11, 2024
d9039eb
Merge branch 'main' of https://github.com/momoth12/Project-Explainabi…
momoth12 Mar 11, 2024
96bd9d1
performance_evaluation
momoth12 Mar 12, 2024
73113ae
adding flow algorithms
nicolassinott Mar 13, 2024
22c6802
flow notebooks
nicolassinott Mar 13, 2024
201a7d4
adding blankout
nicolassinott Mar 13, 2024
057d4c1
attacks_to_be_fix
momoth12 Mar 13, 2024
97b1fdf
attacks_fixed
momoth12 Mar 13, 2024
8e64c13
attacks_update
momoth12 Mar 13, 2024
ec34571
adding new evaluation images
nicolassinott Mar 14, 2024
adf79a3
clear
momoth12 Mar 14, 2024
732bfd7
checkpoint
momoth12 Mar 14, 2024
a8fdca7
last_changes
momoth12 Mar 14, 2024
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5 changes: 5 additions & 0 deletions .gitignore
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attackk.ipynb
imagenet_class_index.json
random_hundred_paths_val.npy
.gitingore
adversaries.py
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Studying PyMaxflow"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook aims to explore the library PyMaxflow necessary for our Attention Flow algorithm"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 2, 13)\n"
]
}
],
"source": [
"import maxflow\n",
"print(maxflow.__version__)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Terminal nodes seem to not be declared in the graph"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"g = maxflow.Graph[int](2,2)\n",
"\n",
"'''\n",
"The constructor parameters (2, 2) are initial estimations of the number of nodes \n",
"and the number of non-terminal edges. These estimations do not need to be correct \n",
"or even approximate (it is possible to set them to 0), but a good estimation allows \n",
"for more efficient memory management.\n",
"'''\n",
"\n",
"# Thus this only talks about capacity\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"nodes = g.add_nodes(2)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"g.add_edge(nodes[0], nodes[1], 1, 2)\n",
"# Hence 1 capacity from node 0 to 1\n",
"# and 2 capacity from node 1 to 0\n",
"\n",
"# Set the capcities of the terminal edges\n",
"g.add_tedge(nodes[0], 2, 5) \n",
"# Hence here it states that it can receive 2\n",
"# and give 5 to the sink\n",
"g.add_tedge(nodes[1], 9, 4) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Conclusion: The non-terminal edges are created with add_edge. The terminal edges are created with add_tedge.\n",
"\n",
"Important question: can it handle the max flow and say how much it went to each sink??"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"initial_graph = g.get_nx_graph()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maximum flow: 8\n"
]
}
],
"source": [
"flow = g.maxflow()\n",
"print(f\"Maximum flow: {flow}\")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Segment of the node 0: 1\n",
"Segment of the node 1: 0\n"
]
}
],
"source": [
"print(f\"Segment of the node 0: {g.get_segment(nodes[0])}\")\n",
"print(f\"Segment of the node 1: {g.get_segment(nodes[1])}\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"final_graph = g.get_nx_graph()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Seems that the only way to compute the output is as such:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdjacencyView({0: {1: {'weight': 1}, 't': {'weight': 3}}, 1: {0: {'weight': 2}}, 's': {1: {'weight': 5}}, 't': {}})"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"initial_graph.adj"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdjacencyView({0: {1: {'weight': 3}, 't': {'weight': 1}}, 1: {}, 's': {1: {'weight': 3}}, 't': {}})"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_graph.adj"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems that the best way is therefore to flatten all attention layers, but in some way keep their weight..."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv_ml_env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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