Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
D
DeepInverse
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Requirements
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Locked files
Build
Pipelines
Jobs
Pipeline schedules
Test cases
Artifacts
Deploy
Releases
Package registry
Container registry
Model registry
Operate
Environments
Terraform modules
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Code review analytics
Issue analytics
Insights
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Terms and privacy
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Hailu, Dawit
DeepInverse
Commits
b14ebf1f
Commit
b14ebf1f
authored
4 years ago
by
Dawit Hailu
Browse files
Options
Downloads
Patches
Plain Diff
updated work
parent
126e0b19
No related branches found
No related tags found
No related merge requests found
Changes
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
Untitled.ipynb
+274
-0
274 additions, 0 deletions
Untitled.ipynb
with
274 additions
and
0 deletions
Untitled.ipynb
0 → 100644
+
274
−
0
View file @
b14ebf1f
{
"cells": [
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fdb6ec17610>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import dival.datasets.lodopab_dataset as lodopab\n",
"import matplotlib.pyplot as plt\n",
"\n",
"dataset = lodopab.LoDoPaBDataset(impl='skimage')\n",
"sample_observ, sample_ground_truth = dataset.get_sample(1231)\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(sample_observ)\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(sample_ground_truth)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Net(\n",
" (inputlayer): Linear(in_features=513000, out_features=10, bias=True)\n",
" (layer2): Linear(in_features=10, out_features=131044, bias=True)\n",
")\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"'''\n",
"input: 1000*513\n",
"output: 362*362\n",
"\n",
"'''\n",
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.inputlayer = nn.Linear(1000*513, 10, True)\n",
" self.layer2 = nn.Linear(10,362*362, True)\n",
" \n",
" def forward(self, inp): #inp is a Vector of inputsize\n",
" x = self.inputlayer(inp)\n",
" x = F.relu(x)\n",
" x = self.layer2(x)\n",
" return x\n",
"\n",
"mynet = Net()\n",
"print(mynet)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([0.7736, 0.7270, 0.2761, ..., 0.7862, 0.7033, 0.8556])\n",
"torch.Size([131044])\n"
]
},
{
"ename": "AttributeError",
"evalue": "'DiscreteLpElement' object has no attribute 'type'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-0d3c77efb4fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmynet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#131 044 = 362*362\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'DiscreteLpElement' object has no attribute 'type'"
]
}
],
"source": [
"'''data=dataset.get_sample(1230)\n",
"print(torch.is_tensor(data))\n",
"data2=torch.as_tensor(data)\n",
"print(torch.is_tensor(data2))\n",
"'''\n",
"data=torch.rand(1000*513)\n",
"print(data)\n",
"print(mynet(data).size()) #131 044 = 362*362\n",
"print(dataset.get_sample(1)[0].type())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "TypeError",
"evalue": "expected np.ndarray (got DiscreteLpElement)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-5ef64af544ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorchvision\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1230\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: expected np.ndarray (got DiscreteLpElement)"
]
}
],
"source": [
"from torchvision import transforms\n",
"\n",
"torch.from_numpy(dataset.get_sample(1230)[0])\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([513])\n"
]
}
],
"source": [
"data=dataset.get_sample(1231)\n",
"print(torch.as_tensor(data[0][0]).size())"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "expected np.ndarray (got DiscreteLpElement)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-64-741f17b0fc5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m ])\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#Transformer(data[1][0])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: expected np.ndarray (got DiscreteLpElement)"
]
}
],
"source": [
"import numpy as np\n",
"Transformer = transforms.Compose([\n",
" transforms.ToTensor()\n",
"])\n",
"#Transformer(data[1][0])\n",
"torch.from_numpy(data[1])"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([-7.1809e-05, -4.1940e-05, -2.4069e-04, ..., 8.4311e-05,\n",
" 2.3020e-04, 1.7529e-04])\n",
"tensor([[[-7.1809e-05, -4.1940e-05, -2.4069e-04, ..., -1.0458e-04,\n",
" 1.8018e-05, 1.4489e-04],\n",
" [-1.1648e-04, 6.9213e-05, -1.7874e-04, ..., -1.1945e-04,\n",
" -6.0001e-06, 1.7225e-04],\n",
" [-2.8182e-04, -3.2965e-05, -2.0238e-04, ..., -5.0908e-05,\n",
" -9.5652e-05, -3.8949e-05],\n",
" ...,\n",
" [-3.5957e-05, 6.0031e-06, -2.3186e-04, ..., 4.4923e-04,\n",
" -2.3983e-05, 1.3578e-04],\n",
" [ 3.9072e-05, -2.8475e-04, -7.4792e-05, ..., -4.0148e-04,\n",
" -4.4930e-05, -4.4930e-05],\n",
" [ 3.9072e-05, 2.0577e-04, 4.1504e-04, ..., 8.4311e-05,\n",
" 2.3020e-04, 1.7529e-04]]])\n"
]
}
],
"source": [
"x,y = data=dataset.get_samples(slice(0,1,1))\n",
"print(torch.from_numpy(x).reshape(513*1000))\n",
"print(torch.from_numpy(x))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-5.9897e-02, 2.2034e-01, 1.2248e-01, ..., 2.7456e-05,\n",
" 2.8770e-01, -1.5429e-01], grad_fn=<AddBackward0>)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mynet(torch.from_numpy(x).reshape(513*1000))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
```
python
import
dival.datasets.lodopab_dataset
as
lodopab
import
matplotlib.pyplot
as
plt
dataset
=
lodopab
.
LoDoPaBDataset
(
impl
=
'
skimage
'
)
sample_observ
,
sample_ground_truth
=
dataset
.
get_sample
(
1231
)
plt
.
subplot
(
1
,
2
,
1
)
plt
.
imshow
(
sample_observ
)
plt
.
subplot
(
1
,
2
,
2
)
plt
.
imshow
(
sample_ground_truth
)
```
%% Output
<matplotlib.image.AxesImage at 0x7fdb6ec17610>
%% Cell type:code id: tags:
```
python
import
torch
import
torch.nn
as
nn
import
torch.nn.functional
as
F
'''
input: 1000*513
output: 362*362
'''
class
Net
(
nn
.
Module
):
def
__init__
(
self
):
super
(
Net
,
self
).
__init__
()
self
.
inputlayer
=
nn
.
Linear
(
1000
*
513
,
10
,
True
)
self
.
layer2
=
nn
.
Linear
(
10
,
362
*
362
,
True
)
def
forward
(
self
,
inp
):
#inp is a Vector of inputsize
x
=
self
.
inputlayer
(
inp
)
x
=
F
.
relu
(
x
)
x
=
self
.
layer2
(
x
)
return
x
mynet
=
Net
()
print
(
mynet
)
```
%% Output
Net(
(inputlayer): Linear(in_features=513000, out_features=10, bias=True)
(layer2): Linear(in_features=10, out_features=131044, bias=True)
)
%% Cell type:code id: tags:
```
python
'''
data=dataset.get_sample(1230)
print(torch.is_tensor(data))
data2=torch.as_tensor(data)
print(torch.is_tensor(data2))
'''
data
=
torch
.
rand
(
1000
*
513
)
print
(
data
)
print
(
mynet
(
data
).
size
())
#131 044 = 362*362
print
(
dataset
.
get_sample
(
1
)[
0
].
type
())
```
%% Output
tensor([0.7736, 0.7270, 0.2761, ..., 0.7862, 0.7033, 0.8556])
torch.Size([131044])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-19-0d3c77efb4fa> in <module>
7 print(data)
8 print(mynet(data).size()) #131 044 = 362*362
----> 9 print(dataset.get_sample(1)[0].type())
AttributeError: 'DiscreteLpElement' object has no attribute 'type'
%% Cell type:code id: tags:
```
python
from
torchvision
import
transforms
torch
.
from_numpy
(
dataset
.
get_sample
(
1230
)[
0
])
print
(
data
)
```
%% Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-5ef64af544ca> in <module>
1 from torchvision import transforms
2
----> 3 torch.from_numpy(dataset.get_sample(1230)[0])
4 print(data)
TypeError: expected np.ndarray (got DiscreteLpElement)
%% Cell type:code id: tags:
```
python
data
=
dataset
.
get_sample
(
1231
)
print
(
torch
.
as_tensor
(
data
[
0
][
0
]).
size
())
```
%% Output
torch.Size([513])
%% Cell type:code id: tags:
```
python
import
numpy
as
np
Transformer
=
transforms
.
Compose
([
transforms
.
ToTensor
()
])
#Transformer(data[1][0])
torch
.
from_numpy
(
data
[
1
])
```
%% Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-64-741f17b0fc5e> in <module>
4 ])
5 #Transformer(data[1][0])
----> 6 torch.from_numpy(data[1])
TypeError: expected np.ndarray (got DiscreteLpElement)
%% Cell type:code id: tags:
```
python
x
,
y
=
data
=
dataset
.
get_samples
(
slice
(
0
,
1
,
1
))
print
(
torch
.
from_numpy
(
x
).
reshape
(
513
*
1000
))
print
(
torch
.
from_numpy
(
x
))
```
%% Output
tensor([-7.1809e-05, -4.1940e-05, -2.4069e-04, ..., 8.4311e-05,
2.3020e-04, 1.7529e-04])
tensor([[[-7.1809e-05, -4.1940e-05, -2.4069e-04, ..., -1.0458e-04,
1.8018e-05, 1.4489e-04],
[-1.1648e-04, 6.9213e-05, -1.7874e-04, ..., -1.1945e-04,
-6.0001e-06, 1.7225e-04],
[-2.8182e-04, -3.2965e-05, -2.0238e-04, ..., -5.0908e-05,
-9.5652e-05, -3.8949e-05],
...,
[-3.5957e-05, 6.0031e-06, -2.3186e-04, ..., 4.4923e-04,
-2.3983e-05, 1.3578e-04],
[ 3.9072e-05, -2.8475e-04, -7.4792e-05, ..., -4.0148e-04,
-4.4930e-05, -4.4930e-05],
[ 3.9072e-05, 2.0577e-04, 4.1504e-04, ..., 8.4311e-05,
2.3020e-04, 1.7529e-04]]])
%% Cell type:code id: tags:
```
python
mynet
(
torch
.
from_numpy
(
x
).
reshape
(
513
*
1000
))
```
%% Output
tensor([-5.9897e-02, 2.2034e-01, 1.2248e-01, ..., 2.7456e-05,
2.8770e-01, -1.5429e-01], grad_fn=<AddBackward0>)
%% Cell type:code id: tags:
```
python
```
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment