Merge branch 'master' into Charlemagne-low-level-api

wandb · Sep 25, 2020 · 7893e50 · 7893e50
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diff --git a/README.md b/README.md
@@ -83,6 +83,6 @@ We make it easy to cite W&B in your published paper. [Learn more →](https://ww
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diff --git a/standalone_tests/all_media_types.py b/standalone_tests/all_media_types.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python
+
+# Test logging media alone, in dicts, lists, and data frames.
+# Add to both history and summary.
+
+import os
+import subprocess
+
+import numpy
+import pandas
+import PIL
+from pkg_resources import parse_version
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import tensorflow
+import torch
+from torchvision import models
+import wandb
+
+
+def dummy_torch_tensor(size, requires_grad=True):
+    if parse_version(torch.__version__) >= parse_version('0.4'):
+        return torch.ones(size, requires_grad=requires_grad)
+    else:
+        return torch.autograd.Variable(torch.ones(size), requires_grad=requires_grad)
+
+
+def main():
+    wandb.init()
+
+    histogram_small_literal = wandb.Histogram(np_histogram=([1, 2, 4], [3, 10, 20, 0]))
+    histogram_large_random = wandb.Histogram(numpy.random.randint(255, size=(1000)))
+    numpy_array = numpy.random.rand(1000)
+    torch_tensor = torch.rand(1000, 1000)
+    data_frame = pandas.DataFrame(data=numpy.random.rand(1000), columns=['col'])
+    tensorflow_variable_single = tensorflow.Variable(543.01, tensorflow.float32)
+    tensorflow_variable_multi = tensorflow.Variable([[2, 3], [7, 11]], tensorflow.int32)
+    plot_scatter = go.Figure(  # plotly
+        data=go.Scatter(x=[0, 1, 2]),
+        layout=go.Layout(
+            title=go.layout.Title(text="A Bar Chart")))
+
+    image_data = numpy.zeros((28, 28))
+    image_cool = wandb.Image(image_data, caption="Cool zeros")
+    image_nice = wandb.Image(image_data, caption="Nice zeros")
+    image_random = wandb.Image(numpy.random.randint(255, size=(28, 28, 3)))
+    image_pil = wandb.Image(PIL.Image.new("L", (28, 28)))
+    plt.plot([1, 2, 3, 4])
+    plt.ylabel('some interesting numbers')
+    image_matplotlib_plot = wandb.Image(plt)
+    matplotlib_plot = plt
+
+    audio_data = numpy.random.uniform(-1, 1, 44100)
+    sample_rate = 44100
+    caption1 = "This is what a dog sounds like"
+    caption2 = "This is what a chicken sounds like"
+    # test with all captions
+    audio1 = wandb.Audio(audio_data, sample_rate=sample_rate, caption=caption1)
+    audio2 = wandb.Audio(audio_data, sample_rate=sample_rate, caption=caption2)
+    # test with no captions
+    audio3 = wandb.Audio(audio_data, sample_rate=sample_rate)
+    audio4 = wandb.Audio(audio_data, sample_rate=sample_rate)
+    # test with some captions
+    audio5 = wandb.Audio(audio_data, sample_rate=sample_rate)
+    audio6 = wandb.Audio(audio_data, sample_rate=sample_rate, caption=caption2)
+
+    html = wandb.Html("<html><body><h1>Hello</h1></body></html>")
+
+    table_default_columns = wandb.Table()
+    table_default_columns.add_data("Some awesome text", "Positive", "Negative")
+
+    table_custom_columns = wandb.Table(["Foo", "Bar"])
+    table_custom_columns.add_data("So", "Cool")
+    table_custom_columns.add_data("&", "Rad")
+
+    #plot_figure = matplotlib.pyplot.plt.figure()
+    #c1 = matplotlib.pyplot.plt.Circle((0.2, 0.5), 0.2, color='r')
+    #ax = matplotlib.pyplot.plt.gca()
+    #ax.add_patch(c1)
+    #matplotlib.pyplot.plt.axis('scaled')
+
+    # pytorch model graph
+    alex = models.AlexNet()
+    graph = wandb.wandb_torch.TorchGraph.hook_torch(alex)
+    alex.forward(dummy_torch_tensor((2, 3, 224, 224)))
+
+
+    with tensorflow.Session().as_default() as sess:
+        sess.run(tensorflow.global_variables_initializer())
+
+        wandb.run.summary.update({
+            'histogram-small-literal-summary': histogram_small_literal,
+            'histogram-large-random-summary': histogram_large_random,
+            'numpy-array-summary': numpy_array,
+            'torch-tensor-summary': torch_tensor,
+            'data-frame-summary': data_frame,
+
+            'image-cool-summary': image_cool,
+            'image-nice-summary': image_nice,
+            'image-random-summary': image_random,
+            'image-pil-summary': image_pil,
+            'image-plot-summary': image_matplotlib_plot,
+            'image-list-summary': [image_cool, image_nice, image_random, image_pil],
+
+            # Doesn't work, because something has happened to the MPL object (MPL may
+            # be doing magical scope stuff). If you log it right after creating it,
+            # it works fine.
+            # 'matplotlib-plot': matplotlib_plot,
+
+            'audio1-summary': audio1,
+            'audio2-summary': audio2,
+            'audio3-summary': audio3,
+            'audio4-summary': audio4,
+            'audio5-summary': audio5,
+            'audio6-summary': audio6,
+            'audio-list-summary': [audio1, audio2, audio3, audio4, audio5, audio6],
+
+            'html-summary': html,
+
+            'table-default-columns-summary': table_default_columns,
+            'table-custom-columns-summary': table_custom_columns,
+
+            'plot-scatter-summary': plot_scatter,
+            #'plot_figure': plot_figure,
+
+            'tensorflow-variable-single-summary': tensorflow_variable_single,
+            'tensorflow-variable-multi-summary': tensorflow_variable_multi,
+
+            'graph-summary': graph,
+        })
+
+        for i in range(10):
+            wandb.run.history.add({
+                'string': 'string',
+                'histogram-small-literal': histogram_small_literal,
+                'histogram-large-random': histogram_large_random,
+                'numpy-array': numpy_array,
+                'torch-tensor': torch_tensor,
+                #'data-frame': data_frame,  # not supported yet
+
+                'image-cool': image_cool,
+                'image-nice': image_nice,
+                'image-random': image_random,
+                'image-pil': image_pil,
+                'image-plot': image_matplotlib_plot,
+                'image-list': [image_cool, image_nice, image_random, image_pil],
+
+                # 'matplotlib-plot': matplotlib_plot,
+
+                'audio1': audio1,
+                'audio2': audio2,
+                'audio3': audio3,
+                'audio4': audio4,
+                'audio5': audio5,
+                'audio6': audio6,
+                'audio-list': [audio1, audio2, audio3, audio4, audio5, audio6],
+
+                'html': html,
+
+                'table-default-columns': table_default_columns,
+                'table-custom-columns': table_custom_columns,
+
+                'plot-scatter': plot_scatter,
+                #'plot_figure': plot_figure,
+
+                'tensorflow-variable-single': tensorflow_variable_single,
+                'tensorflow-variable-multi': tensorflow_variable_multi,
+
+                #'graph': graph,
+            })
+
+        wandb.run.summary.update({
+            'histogram-small-literal-summary': histogram_small_literal,
+            'histogram-large-random-summary': histogram_large_random,
+            'numpy-array-summary': numpy_array,
+            'torch-tensor-summary': torch_tensor,
+            'data-frame-summary': data_frame,
+
+            'image-cool-summary': image_cool,
+            'image-nice-summary': image_nice,
+            'image-random-summary': image_random,
+            'image-pil-summary': image_pil,
+            'image-plot-summary': image_matplotlib_plot,
+            'image-list-summary': [image_cool, image_nice, image_random, image_pil],
+
+            # 'matplotlib-plot': matplotlib_plot,
+
+            'audio1-summary': audio1,
+            'audio2-summary': audio2,
+            'audio3-summary': audio3,
+            'audio4-summary': audio4,
+            'audio5-summary': audio5,
+            'audio6-summary': audio6,
+            'audio-list-summary': [audio1, audio2, audio3, audio4, audio5, audio6],
+
+            'html-summary': html,
+
+            'table-default-columns-summary': table_default_columns,
+            'table-custom-columns-summary': table_custom_columns,
+
+            'plot-scatter-summary': plot_scatter,
+            #'plot_figure': plot_figure,
+
+            'tensorflow-variable-single-summary': tensorflow_variable_single,
+            'tensorflow-variable-multi-summary': tensorflow_variable_multi,
+
+            'graph-summary': graph,
+        })
+
+        #history.add({
+        #    "tensorflow_variable_single": tensorflow_variable_single,
+        #    "tensorflow_variable_multi": tensorflow_variable_single, 
+        #})
+
+
+if __name__ == '__main__':
+    main()
diff --git a/standalone_tests/keras_tensorboard.py b/standalone_tests/keras_tensorboard.py
@@ -0,0 +1,29 @@
+import wandb
+import numpy as np
+import time
+import tensorflow as tf
+import glob
+import os
+
+#wandb.init(project="tf2", sync_tensorboard=True, resume=True)
+wandb.init(sync_tensorboard=True, resume=True)
+
+wandb.config['nice'] = 'So cool fun'
+
+
+class Logger(tf.keras.callbacks.Callback):
+    def on_epoch_end(self, epoch, logs):
+        time.sleep(2)
+        wandb.log({"wild_metrics": logs, "interval": epoch * 10})
+
+
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Conv2D(
+    3, 3, activation="relu", input_shape=(28, 28, 1)))
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(10, activation="softmax"))
+model.compile(loss="sparse_categorical_crossentropy",
+              optimizer="sgd", metrics=["accuracy"])
+
+model.fit(np.ones((10, 28, 28, 1)), np.ones((10,)), epochs=17,
+          validation_split=0.2, callbacks=[Logger(), tf.keras.callbacks.TensorBoard()])
diff --git a/standalone_tests/mixed_keras.py b/standalone_tests/mixed_keras.py
@@ -0,0 +1,19 @@
+import wandb
+import keras
+import numpy as np
+import tensorflow as tf
+from wandb.keras import WandbCallback
+
+#wandb.init(project="tf2")
+wandb.init()
+
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Conv2D(
+    3, 3, activation="relu", input_shape=(28, 28, 1)))
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(10, activation="softmax"))
+model.compile(loss="sparse_categorical_crossentropy",
+              optimizer="sgd", metrics=["accuracy"])
+
+model.fit(np.ones((10, 28, 28, 1)), np.ones((10,)), epochs=7,
+          validation_split=0.2, callbacks=[WandbCallback()])
diff --git a/standalone_tests/point_cloud.py b/standalone_tests/point_cloud.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+# Numpy Clouds
+# http://app.wandb.ai/nbaryd/client-standalone_tests/runs/ly8g46vm?workspace=user-nbaryd
+
+# 3D models
+# http://app.test/nbaryd/client-standalone_tests/runs/0rb3xwke?workspace=user-nbaryd
+
+import os
+
+import numpy as np
+import wandb
+from math import sin, cos, pi
+
+
+DIR = os.path.dirname(__file__)
+
+
+point_cloud_1 = np.array([[0, 0, 0, 1],
+                          [0, 0, 1, 13],
+                          [0, 1, 0, 2],
+                          [0, 1, 0, 4]])
+
+point_cloud_2 = np.array([[0, 0, 0],
+                          [0, 0, 1],
+                          [0, 1, 0],
+                          [0, 1, 0]])
+
+# Generate a symetric pattern
+POINT_COUNT = 20000
+
+# Choose a random sample
+theta_chi = pi * np.random.rand(POINT_COUNT, 2)
+
+
+def gen_point(theta, chi, i):
+    p = sin(theta) * 4.5 * sin(i + 1 / 2 * (i * i + 2)) + \
+        cos(chi) * 7 * sin((2 * i - 4) / 2 * (i + 2))
+
+    x = p * sin(chi) * cos(theta)
+    y = p * sin(chi) * sin(theta)
+    z = p * cos(chi)
+
+    r = sin(theta) * 120 + 120
+    g = sin(x) * 120 + 120
+    b = cos(y) * 120 + 120
+
+    return [x, y, z, r, g, b]
+
+
+def wave_pattern(i):
+    return np.array([gen_point(theta, chi, i) for [theta, chi] in theta_chi])
+
+
+wandb.init()
+
+# Tests 3d OBJ
+
+#wandb.log({"gltf": wandb.Object3D(open(os.path.join(DIR, "../tests/fixtures/Duck.gltf"))),
+#           "obj": wandb.Object3D(open(os.path.join(DIR, "../tests/fixtures/cube.obj")))})
+
+# Tests numpy clouds
+for i in range(0, 20, 10):
+    wandb.log({"Clouds": [wandb.Object3D(point_cloud_1), wandb.Object3D(point_cloud_2)],
+               "Colored_Cloud": wandb.Object3D(wave_pattern(i))})
diff --git a/standalone_tests/pytorch_tensorboard.py b/standalone_tests/pytorch_tensorboard.py
@@ -0,0 +1,38 @@
+import wandb
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+
+wandb.init(tensorboard=True)
+
+
+class ConvNet(nn.Module):
+    def __init__(self):
+        super(ConvNet, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+writer = SummaryWriter()
+net = ConvNet()
+wandb.watch(net, log_freq=2)
+for i in range(10):
+    output = net(torch.ones((64, 1, 28, 28)))
+    loss = F.mse_loss(output, torch.ones((64, 10)))
+    output.backward(torch.ones(64, 10))
+    writer.add_scalar("loss", loss / 64, i+1)
+    writer.add_image("example", torch.ones((1, 28, 28)), i+1)
+writer.close()