-
Notifications
You must be signed in to change notification settings - Fork 21
/
tutorial_unet.html
155 lines (106 loc) · 6.28 KB
/
tutorial_unet.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
---
title: How to create a Unet architecture in `timm` utilizing all the available backbones?
keywords: fastai
sidebar: home_sidebar
nb_path: "nbs/06c_tutorial_unet.ipynb"
---
<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: nbs/06c_tutorial_unet.ipynb
# command to build the docs after a change: nbdev_build_docs
-->
<div class="container" id="notebook-container">
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">timm</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
</pre></div>
</div>
</div>
</div>
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">UNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">backbone_name</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">backbone</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span>
<span class="n">backbone_name</span><span class="p">,</span>
<span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">,</span>
<span class="n">features_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">out_indices</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'resnet34'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">features_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">out_indices</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="p">[</span><span class="n">o</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">out</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[torch.Size([1, 128, 28, 28]),
torch.Size([1, 256, 14, 14]),
torch.Size([1, 512, 7, 7])]</pre>
</div>
</div>
</div>
</div>
</div>
{% endraw %}
{% raw %}
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">timm</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="s1">'resnet34'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">features_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="p">[</span><span class="n">o</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">out</span><span class="p">]</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>[torch.Size([1, 64, 112, 112]),
torch.Size([1, 64, 56, 56]),
torch.Size([1, 128, 28, 28]),
torch.Size([1, 256, 14, 14]),
torch.Size([1, 512, 7, 7])]</pre>
</div>
</div>
</div>
</div>
</div>
{% endraw %}
</div>