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Update for Feb. 2020 MEAP release
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-14282
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‎data/p1ch4/jane-austin/1342-0.txt‎ renamed to ‎data/p1ch4/jane-austen/1342-0.txt‎

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‎data/p3ch15/cls_val_example.pt‎

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‎data/part2/luna/annotations_with_malignancy.csv‎

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‎p1ch3/1_tensors.ipynb‎

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}
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],
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"source": [
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"import torch\n",
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"a = torch.ones(3)\n",
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"import torch # <1>\n",
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"a = torch.ones(3) # <2>\n",
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"a"
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]
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},
@@ -274,7 +274,7 @@
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}
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],
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"source": [
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"points = torch.FloatTensor([[4.0, 1.0], [5.0, 3.0], [2.0, 1.0]])\n",
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"points = torch.tensor([[4.0, 1.0], [5.0, 3.0], [2.0, 1.0]])\n",
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"points"
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]
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},
@@ -1022,10 +1022,22 @@
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([0., 0., 0., 0., 0.], dtype=torch.float64)"
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]
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},
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"execution_count": 51,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"points = torch.randn(10, 2) # <1>\n",
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"short_points = points.type(torch.short)"
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"points_64 = torch.rand(5, dtype=torch.double) # <1>\n",
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"points_short = points_64.to(torch.short)\n",
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"points_64 * points_short # works from PyTorch 1.3 onwards"
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]
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},
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{
@@ -1072,7 +1084,9 @@
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{
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"data": {
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"text/plain": [
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"tensor([5., 2.])"
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"tensor([[[4., 1.],\n",
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" [5., 3.],\n",
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" [2., 1.]]])"
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]
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},
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"execution_count": 54,
@@ -1083,7 +1097,8 @@
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"source": [
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"points[1:] # <1>\n",
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"points[1:, :] # <2>\n",
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"points[1:, 0] # <3>"
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"points[1:, 0] # <3>\n",
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"points[None] # <4>"
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]
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},
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{

‎p1ch3/2_named_tensors.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"../c10/core/TensorImpl.h:860: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable.\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"_ = torch.tensor([0.2126, 0.7152, 0.0722], names=['c'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"img_t = torch.randn(3, 5, 5) # shape [channels, rows, columns]\n",
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"weights = torch.tensor([0.2126, 0.7152, 0.0722])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_t = torch.randn(2, 3, 5, 5) # shape [batch, channels, rows, columns]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([5, 5]), torch.Size([2, 5, 5]))"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"img_gray_naive = img_t.mean(-3)\n",
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"batch_gray_naive = batch_t.mean(-3)\n",
59+
"img_gray_naive.shape, batch_gray_naive.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([2, 3, 5, 5]), torch.Size([2, 3, 5, 5]), torch.Size([3, 1, 1]))"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"unsqueezed_weights = weights.unsqueeze(-1).unsqueeze_(-1)\n",
80+
"img_weights = (img_t * unsqueezed_weights)\n",
81+
"batch_weights = (batch_t * unsqueezed_weights)\n",
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"img_gray_weighted = img_weights.sum(-3)\n",
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"batch_gray_weighted = batch_weights.sum(-3)\n",
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"batch_weights.shape, batch_t.shape, unsqueezed_weights.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([2, 5, 5])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"img_gray_weighted_fancy = torch.einsum('...chw,c->...hw', img_t, weights)\n",
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"batch_gray_weighted_fancy = torch.einsum('...chw,c->...hw', batch_t, weights)\n",
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"batch_gray_weighted_fancy.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([0.2126, 0.7152, 0.0722], names=('channels',))"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"weights_named = torch.tensor([0.2126, 0.7152, 0.0722], names=['channels'])\n",
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"weights_named"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"img named: torch.Size([3, 5, 5]) ('channels', 'rows', 'columns')\n",
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"batch named: torch.Size([2, 3, 5, 5]) (None, 'channels', 'rows', 'columns')\n"
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]
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}
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],
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"source": [
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"img_named = img_t.refine_names(..., 'channels', 'rows', 'columns')\n",
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"batch_named = batch_t.refine_names(..., 'channels', 'rows', 'columns')\n",
151+
"print(\"img named:\", img_named.shape, img_named.names)\n",
152+
"print(\"batch named:\", batch_named.shape, batch_named.names)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([3, 1, 1]), ('channels', 'rows', 'columns'))"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"weights_aligned = weights_named.align_as(img_named)\n",
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"weights_aligned.shape, weights_aligned.names"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([5, 5]), ('rows', 'columns'))"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"gray_named = (img_named * weights_aligned).sum('channels')\n",
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"gray_named.shape, gray_named.names"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Error when attempting to broadcast dims ['channels', 'rows', 'columns'] and dims ['channels']: dim 'columns' and dim 'channels' are at the same position from the right but do not match.\n"
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]
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}
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],
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"source": [
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"try:\n",
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" gray_named = (img_named[..., :3] * weights_named).sum('channels')\n",
219+
"except Exception as e:\n",
220+
" print(e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([5, 5]), (None, None))"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"gray_plain = gray_named.rename(None)\n",
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"gray_plain.shape, gray_plain.names"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎p1ch4/5_image_dog.ipynb‎ renamed to ‎p1ch4/1_image_dog.ipynb‎

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"outputs": [],
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"source": [
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"img = torch.from_numpy(img_arr)\n",
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"out = torch.transpose(img, 0, 2)"
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"out = img.permute(2, 0, 1)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 100\n",
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"batch = torch.zeros(100, 3, 256, 256, dtype=torch.uint8)"
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"batch_size = 3\n",
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"batch = torch.zeros(batch_size, 3, 256, 256, dtype=torch.uint8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"data_dir = '../data/p1ch4/image-cats/'\n",
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"filenames = [name for name in os.listdir(data_dir) if os.path.splitext(name)[-1] == '.png']\n",
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"for i, filename in enumerate(filenames):\n",
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" img_arr = imageio.imread(filename)\n",
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" batch[i] = torch.transpose(torch.from_numpy(img_arr), 0, 2)"
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" img_arr = imageio.imread(os.path.join(data_dir, filename))\n",
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" img_t = torch.from_numpy(img_arr)\n",
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" img_t = img_t.permute(2, 0, 1)\n",
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" img_t = img_t[:3] # <1>\n",
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" batch[i] = img_t"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.7.6"
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}
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},
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"nbformat": 4,

‎p1ch4/6_volumetric_ct.ipynb‎ renamed to ‎p1ch4/2_volumetric_ct.ipynb‎

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