cleanup of U-net architecture
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-78
+29
-78
@@ -80,8 +80,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Torch version 1.8.1+cu101\n",
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"Downloading training data (300MB), this can take a few minutes the first time...\n",
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"Torch version 1.7.1\n",
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"Loaded data, 320 training, 80 validation samples\n",
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"Size of the inputs array: (320, 3, 128, 128)\n"
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]
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@@ -151,14 +150,13 @@
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1152x360 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light",
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"tags": []
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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@@ -282,14 +280,9 @@
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"import torch.autograd \n",
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"import torch.utils.data \n",
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"\n",
|
||||
"def blockUNet(in_c, out_c, name, transposed=False, bn=True, relu=True, size=4, pad=1, dropout=0.):\n",
|
||||
"def blockUNet(in_c, out_c, name, size=4, pad=1, transposed=False, bn=True, activation=True, relu=True, dropout=0. ):\n",
|
||||
" block = nn.Sequential()\n",
|
||||
"\n",
|
||||
" if relu:\n",
|
||||
" block.add_module('%s_relu' % name, nn.ReLU(inplace=True))\n",
|
||||
" else:\n",
|
||||
" block.add_module('%s_leakyrelu' % name, nn.LeakyReLU(0.2, inplace=True))\n",
|
||||
"\n",
|
||||
" if not transposed:\n",
|
||||
" block.add_module('%s_conv' % name, nn.Conv2d(in_c, out_c, kernel_size=size, stride=2, padding=pad, bias=True))\n",
|
||||
" else:\n",
|
||||
@@ -302,6 +295,12 @@
|
||||
" if dropout>0.:\n",
|
||||
" block.add_module('%s_dropout' % name, nn.Dropout2d( dropout, inplace=True))\n",
|
||||
"\n",
|
||||
" if activation:\n",
|
||||
" if relu:\n",
|
||||
" block.add_module('%s_relu' % name, nn.ReLU(inplace=True))\n",
|
||||
" else:\n",
|
||||
" block.add_module('%s_leakyrelu' % name, nn.LeakyReLU(0.2, inplace=True))\n",
|
||||
"\n",
|
||||
" return block\n",
|
||||
" \n",
|
||||
"class DfpNet(nn.Module):\n",
|
||||
@@ -309,9 +308,7 @@
|
||||
" super(DfpNet, self).__init__()\n",
|
||||
" channels = int(2 ** channelExponent + 0.5)\n",
|
||||
"\n",
|
||||
" self.layer1 = nn.Sequential()\n",
|
||||
" self.layer1.add_module('layer1', nn.Conv2d(3, channels, 4, 2, 1, bias=True))\n",
|
||||
"\n",
|
||||
" self.layer1 = blockUNet(3 , channels*1, 'enc_layer1', transposed=False, bn=True, relu=False, dropout=dropout )\n",
|
||||
" self.layer2 = blockUNet(channels , channels*2, 'enc_layer2', transposed=False, bn=True, relu=False, dropout=dropout )\n",
|
||||
" self.layer3 = blockUNet(channels*2, channels*2, 'enc_layer3', transposed=False, bn=True, relu=False, dropout=dropout )\n",
|
||||
" self.layer4 = blockUNet(channels*2, channels*4, 'enc_layer4', transposed=False, bn=True, relu=False, dropout=dropout )\n",
|
||||
@@ -326,10 +323,7 @@
|
||||
" self.dlayer4 = blockUNet(channels*8, channels*2, 'dec_layer4', transposed=True, bn=True, relu=True, dropout=dropout )\n",
|
||||
" self.dlayer3 = blockUNet(channels*4, channels*2, 'dec_layer3', transposed=True, bn=True, relu=True, dropout=dropout )\n",
|
||||
" self.dlayer2 = blockUNet(channels*4, channels , 'dec_layer2', transposed=True, bn=True, relu=True, dropout=dropout )\n",
|
||||
"\n",
|
||||
" self.dlayer1 = nn.Sequential()\n",
|
||||
" self.dlayer1.add_module('dec_layer1_relu', nn.ReLU(inplace=True))\n",
|
||||
" self.dlayer1.add_module('dec_layer1_tconv', nn.ConvTranspose2d(channels*2, 3, 4, 2, 1, bias=True))\n",
|
||||
" self.dlayer1 = blockUNet(channels*2, 3 , 'dec_layer1', transposed=True, bn=False, activation=False, dropout=dropout )\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" # note, this Unet stack could be allocated with a loop, of course... \n",
|
||||
@@ -362,8 +356,7 @@
|
||||
" m.weight.data.normal_(0.0, 0.02)\n",
|
||||
" elif classname.find('BatchNorm') != -1:\n",
|
||||
" m.weight.data.normal_(1.0, 0.02)\n",
|
||||
" m.bias.data.fill_(0)\n",
|
||||
"\n"
|
||||
" m.bias.data.fill_(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -392,7 +385,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Trainable params: 147555 -> crucial! always keep in view... \n"
|
||||
"Trainable params: 147363 -> crucial! always keep in view... \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -454,28 +447,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training from scratch\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch: 0, L1 train: 0.20192, L1 vali: 0.17916\n",
|
||||
"Epoch: 1, L1 train: 0.18912, L1 vali: 0.18013\n",
|
||||
"Epoch: 2, L1 train: 0.17917, L1 vali: 0.17145\n",
|
||||
"Epoch: 20, L1 train: 0.08637, L1 vali: 0.08432\n",
|
||||
"Epoch: 40, L1 train: 0.03682, L1 vali: 0.03222\n",
|
||||
"Epoch: 60, L1 train: 0.03176, L1 vali: 0.02710\n",
|
||||
"Epoch: 80, L1 train: 0.02772, L1 vali: 0.02522\n",
|
||||
"Training done, saved trained network\n"
|
||||
"Found existing network, loading & skipping training\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -555,14 +527,13 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
@@ -615,20 +586,17 @@
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
"text": []
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
@@ -685,62 +653,45 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading test data, this should be fast...\n",
|
||||
"Loaded 10/10 test samples\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Average test error: 0.029000501427799464\n"
|
||||
"\n",
|
||||
"Average test error: 0.027735761646181346\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x360 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light",
|
||||
"tags": []
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
@@ -836,4 +787,4 @@
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
}
|
||||
Reference in new issue
Block a user