Working one the cnn lstm ctc model.

1 files changed, 289 insertions, 34 deletions

diff --git a/src/notebooks/02c-image-patches.ipynb b/src/notebooks/02c-image-patches.ipynb
index f8dcc4c..e1772a9 100644
--- a/src/notebooks/02c-image-patches.ipynb
+++ b/src/notebooks/02c-image-patches.ipynb
@@ -38,7 +38,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-08-09 20:45:35.945 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:160 - EmnistLinesDataset loading data from HDF5...\n"
+      "2020-08-11 19:26:46.938 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:159 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
        "torch.Size([1, 28, 952])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -246,7 +246,7 @@
        "torch.Size([1, 28, 952])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
        "torch.Size([34])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,16 +286,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([1, 67, 28, 28])"
+       "torch.Size([1, 67, 1, 28, 28])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,14 +359,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-08-09 22:34:07.398 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:159 - EmnistLinesDataset loading data from HDF5...\n"
+      "2020-08-11 19:28:39.339 | DEBUG    | text_recognizer.datasets.emnist_lines_dataset:_load_data:159 - EmnistLinesDataset loading data from HDF5...\n"
      ]
     }
    ],
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -403,7 +403,7 @@
        "torch.Size([16, 1, 28, 952])"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -423,7 +423,7 @@
        "torch.Size([16, 34])"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,16 +443,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([16, 67, 28, 28])"
+       "torch.Size([16, 67, 1, 28, 28])"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,22 +463,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "and the washedout final___________\n"
+     ]
+    },
+    {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa06f865d90>"
+       "<matplotlib.image.AxesImage at 0x7febf2ec4400>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x1440 with 1 Axes>"
       ]
@@ -491,17 +498,20 @@
    ],
    "source": [
     "plt.figure(figsize=(20, 20))\n",
+    "sentence = convert_y_label_to_string(t[0].numpy()) \n",
+    "print(sentence)\n",
+    "plt.title(sentence)\n",
     "plt.imshow(d[0, 0], cmap='gray')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1440x1440 with 5 Axes>"
       ]
@@ -520,6 +530,251 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reshape patches for passing through a CNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from einops import rearrange"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = rearrange(patches, \"b t c h w -> (b t) c h w\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1072, 1, 28, 28])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnn = nn.Conv2d(1, 16, 3, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx = cnn(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1072, 16, 9, 9])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xx.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = rearrange(xx, \"(b t) c h w -> (b t) (c h w)\", b = 16, t=67)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = rearrange(xx, \"(b t) c h w -> b t (c h w)\", b = 16, t=67)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1072, 1296])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = nn.Linear(1296, 128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hh = l(h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1072, 128])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hh.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hhh = rearrange(hh, \"(b t) h -> t b h\", b = 16, t=67)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([67, 16, 128])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hhh.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rnn = nn.LSTM(128, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h0 = torch.randn(1, 16, 64)\n",
+    "c0 = torch.randn(1, 16, 64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output, (hn, cn) = rnn(hhh, (h0, c0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([67, 16, 64])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.shape"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},