Added Lab 2 notebook

.gitignore

@@ -0,0 +1 @@
+.DS_Store

Lab 2/Lifecycle_configurations.sh

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+#!/bin/bash
+
+set -e
+
+# OVERVIEW
+# This script installs all necessary software for running the DeepComposer GAN notebook.
+
+sudo -u ec2-user -i <<'EOF'
+ENVIRONMENT=python3
+source /home/ec2-user/anaconda3/bin/activate "$ENVIRONMENT"
+
+conda update --all --y 
+pip install tensorflow==1.14.0
+pip install numpy==1.16.4
+pip install pretty_midi
+pip install pypianoroll
+pip install music21
+pip install seaborn
+pip install --ignore-installed moviepy
+
+source /home/ec2-user/anaconda3/bin/deactivate
+EOF

Lab 2/README.md

@@ -8,26 +8,40 @@ Navigate to Amazon SageMaker using the link: https://console.aws.amazon.com/sage
 
 Click **Notebook instances** from the left navigation bar
 
-![open-instance](images/open-instance.PNG)
+Select **Create notebook instance**
+
+![create-notebook](images/create_notebook.png)
+
+Within the notebook instance creation form, select "c5.4xlarge" for **Notebook instance type**
+
+![notebook-instance-settings](images/notebook_instance_settings.png)
+
+Set the following for **Permissions and encryption**:
+* IAM role: Use an existing role or create a new role
+* Root access: Enable
+* Encryption key: No Custom Encryption
+
+![notebook-instance-settings](images/permissions_and_encryption.png)
+
+Set the following for **Git repositories**:
+* Repository: Clone a public Git repository to this notebook instance only
+* Git repository URL: https://github.com/aws-samples/aws-deepcomposer-samples
+
+![notebook-instance-settings](images/notebook_git_settings.png)
 
 Click **Open Jupyter**
 
-![open-notebook](images/open-notebook.PNG)
+![open-notebook](images/open_jupyter.png)
 
-Click **DeepComposerWorkshop** folder
+Click **Lab 2** folder, then click **GAN.ipynb**
 
 ![GAN-notebook](images/GAN-notebook.PNG)
 
-Click **GAN.ipynb**
 
-![kernel-error](images/kernel-error.PNG)
-
-*If you come across the above error, choose the drop down and select **conda_python3** as the kernel*
+*You will likely be prompted to select kernel. Choose the drop down and select **conda_python3** as the kernel*
 
 ![set-kernel](images/set-kernel.PNG)
 
-Select **conda_python3** as the kernel and click **set kernel**
-
 This notebook contains instructions and code to create a custom GAN model from scratch. Scroll through the notebook content.
 
 ![run-notebook](images/run-notebook.PNG)

Lab 2/utils/display_utils.py

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+# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import os
+import numpy as np
+import itertools
+import pretty_midi
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import moviepy.editor as mpy
+import music21
+from IPython.display import Video
+from IPython import display
+import seaborn as sns
+
+# --- plot------------------------------------------------------------------
+
+
+def plot_loss_logs(G_loss, D_loss, figsize=(15, 5), smoothing=0.001):
+    """Utility for plotting losses with smoothing."""
+    plt.ion()
+    sns.set()
+    plt.figure(figsize=figsize)
+    plt.plot(D_loss, label='C_loss')
+    plt.plot(G_loss, label='G_loss')
+    plt.legend(loc='lower right', fontsize='medium')
+    plt.xlabel('Iteration', fontsize='x-large')
+    plt.ylabel('Losses', fontsize='x-large')
+    plt.title('Training History', fontsize='xx-large')
+
+
+def playmidi(filename):
+    mf = music21.midi.MidiFile()
+    mf.open(filename)
+    mf.read()
+    mf.close()
+    s = music21.midi.translate.midiFileToStream(mf)
+    s.show('midi')
+
+def show_pianoroll(xs, min_pitch=45, max_pitch=85,
+                   programs = [0, 0, 0, 0], save_dir=None):
+    """ Plot a MultiTrack PianoRoll
+
+    :param x: Multi Instrument PianoRoll Tensor
+    :param min_pitch: Min Pitch / Min Y across all instruments.
+    :param max_pitch: Max Pitch / Max Y across all instruments.
+    :param programs: Program Number of the Tracks.
+    :param file_name: Optional. File Name to save the plot.
+    :return:
+    """
+
+    # Convert fake_x to numpy and convert -1 to 0
+    xs = xs > 0
+
+    channel_last = lambda x: np.moveaxis(np.array(x), 2, 0)
+    xs = [channel_last(x) for x in xs]
+
+    assert len(xs[0].shape) == 3, 'Pianoroll shape must have 3 dims, Got %d' % len(xs[0].shape)
+    n_tracks, time_step, _ = xs[0].shape
+
+    plt.ion()
+    fig = plt.figure(figsize=(15, 4))
+
+    x = xs[0]
+
+    for j in range(4):
+        b = j+1
+        ax = fig.add_subplot(1,4,b)
+        nz = np.nonzero(x[b-1])
+
+        if programs:
+            ax.set_xlabel('Time('+pretty_midi.program_to_instrument_class(programs[j%4])+')')
+
+        if (j+1)== 1:
+            ax.set_ylabel('Pitch')
+        else:
+            ax.set_yticks([])
+
+        ax.scatter(nz[0], nz[1], s=np.pi * 3, color='bgrcmk'[b-1])
+        ax.set_ylim(45, 85)
+        ax.set_xlim(0, time_step)
+        fig.add_subplot(ax)
+
+
+def plot_pianoroll(iteration, xs, fake_xs, min_pitch=45, max_pitch=85,
+                   programs = [0, 0, 0, 0], save_dir=None):
+    """ Plot a MultiTrack PianoRoll
+
+    :param x: Multi Instrument PianoRoll Tensor
+    :param min_pitch: Min Pitch / Min Y across all instruments.
+    :param max_pitch: Max Pitch / Max Y across all instruments.
+    :param programs: Program Number of the Tracks.
+    :param file_name: Optional. File Name to save the plot.
+    :return:
+    """
+
+    # Convert fake_x to numpy and convert -1 to 0
+    xs = xs > 0
+    fake_xs = fake_xs > 0
+
+    channel_last = lambda x: np.moveaxis(np.array(x), 2, 0)
+    xs = [channel_last(x) for x in xs]
+    fake_xs = [channel_last(fake_x) for fake_x in fake_xs]
+
+    assert len(xs[0].shape) == 3, 'Pianoroll shape must have 3 dims, Got %d' % len(xs[0].shape)
+    n_tracks, time_step, _ = xs[0].shape
+
+    plt.ion()
+    fig = plt.figure(figsize=(15, 8))
+
+    # gridspec inside gridspec
+    outer_grid = gridspec.GridSpec(2, 2, wspace=0.1, hspace=0.2)
+
+    for i in range(4):
+        inner_grid = gridspec.GridSpecFromSubplotSpec(2, n_tracks,
+                subplot_spec=outer_grid[i], wspace=0.0, hspace=0.0)
+
+        x, fake_x = xs[i], fake_xs[i]
+
+        for j, (a, b) in enumerate(itertools.product([1, 2], [1, 2, 3, 4])):
+
+            ax = fig.add_subplot(inner_grid[j])
+
+            if a == 1:
+                nz = np.nonzero(x[b-1])
+            else:
+                nz = np.nonzero(fake_x[b-1])
+
+            if programs:
+                ax.set_xlabel('Time('+pretty_midi.program_to_instrument_class(programs[j%4])+')')
+
+            if b == 1:
+                ax.set_ylabel('Pitch')
+            else:
+                ax.set_yticks([])
+
+            ax.scatter(nz[0], nz[1], s=np.pi * 3, color='bgrcmk'[b-1])
+            ax.set_ylim(45, 85)
+            ax.set_xlim(0, time_step)
+            fig.add_subplot(ax)
+
+    if isinstance(iteration, int):
+        plt.suptitle('iteration: {}'.format(iteration), fontsize=20)
+        filename = os.path.join(save_dir, 'sample_iteration_%05d.png' % iteration)
+    else:
+        plt.suptitle('Inference', fontsize=20)
+        filename = os.path.join(save_dir, 'sample_inference.png')
+    plt.savefig(filename)
+    plt.close(fig)
+
+def display_loss(iteration, d_losses, g_losses):
+    sns.set()
+    display.clear_output(wait=True)
+    fig = plt.figure(figsize=(15,5))
+    line1, = plt.plot(range(iteration+1), d_losses, 'r')
+    line2, = plt.plot(range(iteration+1), g_losses, 'k')
+    plt.xlabel('Iterations')
+    plt.ylabel('Losses')
+    plt.legend((line1, line2), ('C-loss', 'G-loss'))
+
+    return display.display(fig)
+
+
+def make_training_video(folder_dir):
+    files = sorted([os.path.join(folder_dir, f) for f in os.listdir(folder_dir) if f.endswith('.png')])
+    frames = [mpy.ImageClip(f).set_duration(1) for f in files]  
+    clip = mpy.concatenate_videoclips(frames, method="compose")
+    clip.write_videofile("movie.mp4",fps=15) 
+    return Video("movie.mp4")
+

Lab 2/utils/inference_utils.py

@@ -0,0 +1,61 @@
+# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import tensorflow as tf
+import numpy as np
+
+from utils import path_utils, midi_utils, display_utils
+
+# --- local samples------------------------------------------------------------------
+
+def load_melody_samples(n_sample=10):    
+    """Load the samples used for evaluation."""
+
+    sample_source_path = './dataset/eval.npy'
+
+    data = np.load(sample_source_path)
+    data = np.asarray(data, dtype=np.float32) # {-1, 1}
+
+    random_idx = np.random.choice(len(data), n_sample, replace=False)
+
+    sample_x = data[random_idx]
+
+    sample_z = tf.random.truncated_normal((n_sample, 2, 8, 512))
+
+    print("Loaded {} melody samples".format(len(sample_x)))
+
+    return sample_x, sample_z
+
+# --- Training ------------------------------------------------------------------
+
+def generate_pianoroll(generator, conditioned_track, noise_vector=None):
+    if noise_vector == None:
+        noise_vector = tf.random.truncated_normal((1, 2, 8, 512))
+    return generator((conditioned_track, noise_vector), training=False)
+
+
+def generate_midi(generator, saveto_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid'):
+    conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file)
+    generated_pianoroll = generate_pianoroll(generator, conditioned_track)
+
+    destination_path = path_utils.new_temp_midi_path(saveto_dir=saveto_dir)
+    midi_utils.save_pianoroll_as_midi(generated_pianoroll.numpy(), destination_path=destination_path)
+    return destination_path
+
+def show_generated_pianorolls(generator, eval_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid', n_pr = 4):    
+    conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file)
+    for i in range(n_pr):
+        generated_pianoroll = generate_pianoroll(generator, conditioned_track)
+        display_utils.show_pianoroll(generated_pianoroll)