update fixes

Author: birshert

utils/setup_colab.py

@@ -26,16 +26,24 @@ def setup_week03_1():
 
 
 def setup_week03_2():
+    download_github_code('utils/tqdm_utils.py')
     download_github_code('utils/download_utils.py')
     download_github_code('utils/grading.py')
 
 
 def setup_week04():
     download_github_code('week04/lfw_dataset.py')
+    download_github_code('utils/tqdm_utils.py')
     download_github_code('utils/download_utils.py')
     download_github_code('utils/grading.py')
 
 
 def setup_week05():
     download_github_code('utils/grading.py')
     download_github_code('week05/names.txt')
+
+
+def setup_week06():
+    download_github_code('utils/grading.py')
+    download_github_code('week06/beheaded_inception3.py')
+    download_github_code('week06/grading_utils.py')

week03/week03_finetuning_inception.ipynb

@@ -69,24 +69,19 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import random\n",
     "import tqdm\n",
-    "import itertools\n",
     "import collections\n",
-    "from copy import deepcopy\n",
     "from IPython import display\n",
     "\n",
     "import torch\n",
     "from torch import nn\n",
     "from torch import optim\n",
     "from torch.utils.data import DataLoader\n",
     "\n",
-    "import torchvision\n",
     "from torchvision import datasets, transforms, models\n",
     "\n",
     "from sklearn.model_selection import train_test_split"
@@ -376,7 +371,7 @@
    "source": [
     "image, label = dataset[232]\n",
     "print(label)\n",
-    "imshow(image.permute(1, 2, 0))"
+    "plt.imshow(image.permute(1, 2, 0))"
    ]
   },
   {
@@ -501,7 +496,7 @@
    "source": [
     "image, label = dataset[232]\n",
     "print(image.shape, label)\n",
-    "imshow(image.permute(1, 2, 0))"
+    "plt.imshow(image.permute(1, 2, 0))"
    ]
   },
   {
@@ -1038,4 +1033,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week03/week03_first_cnn_cifar10.ipynb

@@ -137,6 +137,8 @@
    },
    "outputs": [],
    "source": [
+    "import grading\n",
+    "\n",
     "grader = grading.Grader(\n",
     "    assignment_key=\"s1B1I5DuEeeyLAqI7dCYkg\",\n",
     "    all_parts=[\"7W4tu\", \"nQOsg\", \"96eco\"]\n",
@@ -1215,4 +1217,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week04/week04_adversarial.ipynb

@@ -65,7 +65,7 @@
     "import grading\n",
     "\n",
     "grader = grading.Grader(\n",
-    "    assignment_key=\"9TShnp1JEeeGGAoCUnhvuA\",\n",
+    "    assignment_key=\"FCl7G51lEeeeZQ4xJ2nzLA\",\n",
     "    all_parts=[\"ryO01\"]\n",
     ")"
    ]
@@ -643,7 +643,7 @@
    "outputs": [],
    "source": [
     "## GRADED PART, DO NOT CHANGE!\n",
-    "grader.set_answer(\"ryO01\", ans_part1)"
+    "grader.set_answer(\"ryO01\", ans)"
    ]
   },
   {

week04/week04_autoencoders.ipynb

@@ -779,19 +779,6 @@
     "# Submit to Coursera"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "sNCPK31xnpy-"
-   },
-   "outputs": [],
-   "source": [
-    "# token expires every 30 min\n",
-    "COURSERA_TOKEN = ### YOUR TOKEN HERE\n",
-    "COURSERA_EMAIL = ### YOUR EMAIL HERE"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1309,4 +1296,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week05/week05_generating_names_with_rnn.ipynb

@@ -123,7 +123,7 @@
     "# to make them of equal size for further batching\n",
     "pad_token = \"#\"\n",
     "\n",
-    "with open(\"names\") as f:\n",
+    "with open(\"names.txt\") as f:\n",
     "    names = f.read()[:-1].split('\\n')\n",
     "    names = [start_token + name for name in names]"
    ]

week06/beheaded_inception3.py

@@ -0,0 +1,52 @@
+import torch, torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torchvision.models.inception import Inception3
+from warnings import warn
+from torch.utils.model_zoo import load_url
+
+
+class BeheadedInception3(Inception3):
+    """ Like torchvision.models.inception.Inception3 but the head goes separately """
+    
+    def forward(self, x):
+        if self.transform_input:
+            x = x.clone()
+            x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
+            x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
+            x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
+        else: warn("Input isn't transformed")
+        x = self.Conv2d_1a_3x3(x)
+        x = self.Conv2d_2a_3x3(x)
+        x = self.Conv2d_2b_3x3(x)
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        x = self.Conv2d_3b_1x1(x)
+        x = self.Conv2d_4a_3x3(x)
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        x = self.Mixed_5b(x)
+        x = self.Mixed_5c(x)
+        x = self.Mixed_5d(x)
+        x = self.Mixed_6a(x)
+        x = self.Mixed_6b(x)
+        x = self.Mixed_6c(x)
+        x = self.Mixed_6d(x)
+        x = self.Mixed_6e(x)
+        x = self.Mixed_7a(x)
+        x = self.Mixed_7b(x)
+        x_for_attn = x = self.Mixed_7c(x)
+        # 8 x 8 x 2048
+        x = F.avg_pool2d(x, kernel_size=8)
+        # 1 x 1 x 2048
+        x_for_capt = x = x.view(x.size(0), -1)
+        # 2048
+        x = self.fc(x)
+        # 1000 (num_classes)
+        return x_for_attn, x_for_capt, x
+    
+
+def beheaded_inception_v3(transform_input=True):
+    model= BeheadedInception3(transform_input=transform_input)
+    inception_url = 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
+    model.load_state_dict(load_url(inception_url))
+    return model
+

week06/week06_final_project_image_captioning.ipynb

@@ -758,6 +758,33 @@
     "grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Apply trained model\n",
+    "\n",
+    "Let's unpack our pre-trained inception network and see what our model is capable of!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from beheaded_inception3 import beheaded_inception_v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inception = beheaded_inception_v3().eval()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -784,7 +811,7 @@
     "                     t=1, sample=True, max_len=100):\n",
     "    \n",
     "    assert isinstance(image, np.ndarray) and np.max(image) <= 1\\\n",
-    "           and np.min(image) >=0 and image.shape[-1] == 3\n",
+    "           and np.min(image) >= 0 and image.shape[-1] == 3\n",
     "    \n",
     "    image = torch.tensor(image.transpose([2, 0, 1]), dtype=torch.float32)\n",
     "    \n",
@@ -799,7 +826,7 @@
     "        next_word_probs = F.softmax(next_word_logits, -1).detach().numpy()\n",
     "        \n",
     "        \n",
-    "        assert len(next_word_probs.shape) ==1, 'probs must be one-dimensional'\n",
+    "        assert len(next_word_probs.shape) == 1, 'probs must be one-dimensional'\n",
     "        next_word_probs = next_word_probs ** t / np.sum(next_word_probs ** t) # apply temperature\n",
     "\n",
     "        if sample:\n",