lab3 client and v0.7.4

mitdeeplearning/lab3.py

@@ -1,178 +1,55 @@
-import cv2
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-import tensorflow as tf
-import time
-import h5py
-import sys
-import glob
-
-IM_SHAPE = (64, 64, 3)
-
-
-def plot_image_prediction(i, predictions_array, true_label, img):
-    predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
-    plt.grid(False)
-    plt.xticks([])
-    plt.yticks([])
-
-    plt.imshow(np.squeeze(img), cmap=plt.cm.binary)
-
-    predicted_label = np.argmax(predictions_array)
-    if predicted_label == true_label:
-        color = "blue"
-    else:
-        color = "red"
-
-    plt.xlabel(
-        "{} {:2.0f}% ({})".format(
-            predicted_label, 100 * np.max(predictions_array), true_label
-        ),
-        color=color,
-    )
-
-
-def plot_value_prediction(i, predictions_array, true_label):
-    predictions_array, true_label = predictions_array[i], true_label[i]
-    plt.grid(False)
-    plt.xticks([])
-    plt.yticks([])
-    thisplot = plt.bar(range(10), predictions_array, color="#777777")
-    plt.ylim([0, 1])
-    predicted_label = np.argmax(predictions_array)
-
-    thisplot[predicted_label].set_color("red")
-    thisplot[true_label].set_color("blue")
-
-
-class DatasetLoader(tf.keras.utils.Sequence):
-    def __init__(self, data_path, batch_size, training=True):
-        print("Opening {}".format(data_path))
-        sys.stdout.flush()
-
-        self.cache = h5py.File(data_path, "r")
-
-        print("Loading data into memory...")
-        sys.stdout.flush()
-        self.images = self.cache["images"][:]
-        self.labels = self.cache["labels"][:].astype(np.float32)
-        self.image_dims = self.images.shape
-
-        train_inds = np.arange(len(self.images))
-        pos_train_inds = train_inds[self.labels[train_inds, 0] == 1.0]
-        neg_train_inds = train_inds[self.labels[train_inds, 0] != 1.0]
-        if training:
-            self.pos_train_inds = pos_train_inds[: int(0.8 * len(pos_train_inds))]
-            self.neg_train_inds = neg_train_inds[: int(0.8 * len(neg_train_inds))]
-        else:
-            self.pos_train_inds = pos_train_inds[-1 * int(0.2 * len(pos_train_inds)) :]
-            self.neg_train_inds = neg_train_inds[-1 * int(0.2 * len(neg_train_inds)) :]
-
-        np.random.shuffle(self.pos_train_inds)
-        np.random.shuffle(self.neg_train_inds)
-
-        self.train_inds = np.concatenate((self.pos_train_inds, self.neg_train_inds))
-        self.batch_size = batch_size
-        self.p_pos = np.ones(self.pos_train_inds.shape) / len(self.pos_train_inds)
-
-    def get_train_size(self):
-        return self.pos_train_inds.shape[0] + self.neg_train_inds.shape[0]
-
-    def __len__(self):
-        return int(np.floor(self.get_train_size() / self.batch_size))
-
-    def __getitem__(self, index):
-        selected_pos_inds = np.random.choice(
-            self.pos_train_inds, size=self.batch_size // 2, replace=False, p=self.p_pos
-        )
-        selected_neg_inds = np.random.choice(
-            self.neg_train_inds, size=self.batch_size // 2, replace=False
+from openai import OpenAI
+
+class LLMClient: 
+    def __init__(self, model: str, api_key: str, api_base: str = "https://openrouter.ai/api/v1"):
+        self.llm_client = OpenAI(api_key=api_key, base_url=api_base)
+        self.model = model
+
+    def ask(self, user: str, system: str = None, **kwargs):
+        messages = [{"role": "user", "content": user}]
+        if system:
+            messages.insert(0, {"role": "system", "content": system})
+        res = self.llm_client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            **kwargs
         )
-        selected_inds = np.concatenate((selected_pos_inds, selected_neg_inds))
+        return res
 
-        sorted_inds = np.sort(selected_inds)
-        train_img = (self.images[sorted_inds] / 255.0).astype(np.float32)
-        train_label = self.labels[sorted_inds, ...]
-        return np.array(train_img), np.array(train_label)
 
-    def get_n_most_prob_faces(self, prob, n):
-        idx = np.argsort(prob)[::-1]
-        most_prob_inds = self.pos_train_inds[idx[: 10 * n : 10]]
-        return (self.images[most_prob_inds, ...] / 255.0).astype(np.float32)
+yoda_test_text = (
+    "Wisdom, sought by many, found by few, it is. Haste not, patience have. "
+    "For in stillness, answers come. Much to learn, still you have. "
+    "Fear leads to anger; anger, to hate. Down the dark path, guide you it will. "
+    "Trust the Force, you must. Powerful ally it is. Life it creates, surrounds, binds. "
+    "Adventure, excitement, a Jedi craves not these things. Discipline, balance, seek you should. "
+    "Hmm, clearer now is the path, yes? Help you more, I can, if needed it is. "
+    "Endless, the journey of learning is. Stay true to your path, and clarity you will find. "
+    "Remember, the Force flows through all, but your heart determines how it shapes your destiny. "
+    "Much more to teach, I have. Ready, are you? Mmm."
+)
 
-    def get_all_faces(self):
-        return (self.images[self.pos_train_inds] / 255.0).astype(np.float32)
 
-    def return_sample_batch(self):
-        return self.__getitem__(0)
 
+# class Llama(LLMClient):
+#     def __init__(self, api_key: str):
+#         """
+#         Initialize the LlamaFree model client. 
 
-def get_test_faces():
-    cwd = os.path.dirname(__file__)
-    images = {"LF": [], "LM": [], "DF": [], "DM": []}
-    for key in images.keys():
-        files = glob.glob(os.path.join(cwd, "data", "faces", key, "*.png"))
-        for file in sorted(files):
-            image = cv2.resize(cv2.imread(file), (64, 64))[:, :, ::-1] / 255.0
-            images[key].append(image)
+#         LlamaFree is available from LlamaFree. 
+#         Provide your LlamaFree API key (`api_key`) to access.
+#         """
+#         # super().__init__(model="meta-llama/llama-3.2-3b-instruct", api_key=api_key)
+#         super().__init__(model="meta-llama/llama-3.1-8b-instruct", api_key=api_key)
 
-    return images["LF"], images["LM"], images["DF"], images["DM"]
 
+# class LFM40B(LLMClient):
+#     def __init__(self, api_key: str):
+#         """
+#         Initialize the LFM-40B model client. 
 
-def plot_k(imgs, fname=None):
-    fig = plt.figure()
-    fig.subplots_adjust(hspace=0.6)
-    num_images = len(imgs)
-    for img in range(num_images):
-        ax = fig.add_subplot(int(num_images / 5), 5, img + 1)
-        ax.xaxis.set_visible(False)
-        ax.yaxis.set_visible(False)
-        img_to_show = imgs[img]
-        ax.imshow(img_to_show, interpolation="nearest")
-    plt.subplots_adjust(wspace=0.20, hspace=0.20)
-    plt.show()
-    if fname:
-        plt.savefig(fname)
-    plt.clf()
-
-
-def plot_percentile(imgs, fname=None):
-    fig = plt.figure()
-    fig, axs = plt.subplots(1, len(imgs), figsize=(11, 8))
-    for img in range(len(imgs)):
-        ax = axs[img]
-        ax.xaxis.set_visible(False)
-        ax.yaxis.set_visible(False)
-        img_to_show = imgs[img]
-        ax.imshow(img_to_show, interpolation="nearest")
-    if fname:
-        plt.savefig(fname)
-
-
-def plot_accuracy_vs_risk(sorted_images, sorted_uncertainty, sorted_preds, plot_title):
-    num_percentile_intervals = 10
-    num_samples = len(sorted_images) // num_percentile_intervals
-    all_imgs = []
-    all_unc = []
-    all_acc = []
-    for percentile in range(num_percentile_intervals):
-        cur_imgs = sorted_images[
-            percentile * num_samples : (percentile + 1) * num_samples
-        ]
-        cur_unc = sorted_uncertainty[
-            percentile * num_samples : (percentile + 1) * num_samples
-        ]
-        cur_predictions = tf.nn.sigmoid(
-            sorted_preds[percentile * num_samples : (percentile + 1) * num_samples]
-        )
-        avged_imgs = tf.reduce_mean(cur_imgs, axis=0)
-        all_imgs.append(avged_imgs)
-        all_unc.append(tf.reduce_mean(cur_unc))
-        all_acc.append((np.ones((num_samples)) == np.rint(cur_predictions)).mean())
-
-    plt.plot(np.arange(num_percentile_intervals) * 10, all_acc)
-    plt.title(plot_title)
-    plt.show()
-    plt.clf()
-    return all_imgs
+#         LFM-40B is available from Lambda Labs. 
+#         Provide your Lambda Labs API key (`api_key`) to access.
+#         """ 
+#         api_base = "https://api.lambdalabs.com/v1"
+#         super().__init__(model="lfm-40b", api_base=api_base, api_key=api_key)

setup.py

@@ -22,13 +22,13 @@ def get_dist(pkgname):
 setup(
   name = 'mitdeeplearning',         # How you named your package folder (MyLib)
   packages = ['mitdeeplearning'],   # Chose the same as "name"
-  version = '0.7.3',      # Start with a small number and increase it with every change you make
+  version = '0.7.4',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Official software labs for MIT Introduction to Deep Learning (http://introtodeeplearning.com)',   # Give a short description about your library
   author = 'Alexander Amini',                   # Type in your name
   author_email = '[email protected]',      # Type in your E-Mail
   url = 'http://introtodeeplearning.com',   # Provide either the link to your github or to your website
-  download_url = 'https://github.com/MITDeepLearning/introtodeeplearning/archive/v0.7.3.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/MITDeepLearning/introtodeeplearning/archive/v0.7.4.tar.gz',    # I explain this later on
   keywords = ['deep learning', 'neural networks', 'tensorflow', 'introduction'],   # Keywords that define your package best
   install_requires=install_deps,
   classifiers=[