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import os.path
from dataclasses import dataclass
from typing import Tuple, List, Union
import matplotlib
import pandas as pd
from PIL import Image
from matplotlib import axes
from pandas import DataFrame
from pandas.plotting._matplotlib.style import get_standard_colors
from tensorboard.compat.proto import event_pb2
from dreambooth.shared import status
@dataclass
class YAxis:
name: str
columns: List[str]
@dataclass
class PlotDefinition:
title: str
x_axis: str
y_axis: List[YAxis]
@dataclass()
class ParsedValues:
loss: DataFrame
lr: DataFrame
ram: DataFrame
merged: bool
class LogParser:
def __init__(self):
self.logging_dir = None
self.model_name = None
self.parsed = {}
self.out_loss = []
self.out_lr = []
self.out_ram = []
self.parsed_files = {}
self.smoothing_window = 50
# Call this when switching models
def reset(self):
self.parsed = {}
self.out_loss = []
self.out_lr = []
self.out_ram = []
self.parsed_files = {}
def plot_multi_alt(
self,
data: pd.DataFrame,
plot_definition: PlotDefinition,
spacing: float = 0.1,
):
styles = ["-", ":", "--", "-."]
colors = get_standard_colors(num_colors=7)
loss_color = colors[0]
avg_colors = colors[1:]
for i, yi in enumerate(plot_definition.y_axis):
if len(yi.columns) > len(styles):
raise ValueError(
f"Maximum {len(styles)} traces per yaxis allowed. If we want to allow this we need to add some logic.")
if i > len(colors):
raise ValueError(
f"Maximum {len(colors)} yaxis axis allowed. If we want to allow this we need to add some logic.")
if i == 0:
ax = data.plot(
x=plot_definition.x_axis,
y=yi.columns,
title=plot_definition.title,
color=[loss_color] * len(yi.columns)
)
ax.set_ylabel(ylabel=yi.name)
else:
# Multiple y-axes
ax_new = ax.twinx()
ax_new.spines["right"].set_position(("axes", 1 + spacing * (i - 1)))
data.plot(
ax=ax_new,
x=plot_definition.x_axis,
y=yi.columns,
color=[avg_colors[yl] for yl in range(len(yi.columns))]
)
ax_new.set_ylabel(ylabel=yi.name)
ax.legend(loc=0)
return ax
def plot_multi(
self,
data: pd.DataFrame,
x: Union[str, None] = None,
y: Union[List[str], None] = None,
spacing: float = 0.1,
**kwargs
) -> matplotlib.axes.Axes:
"""Plot multiple Y axes on the same chart with same x axis.
Args:
data: dataframe which contains x and y columns
x: column to use as x axis. If None, use index.
y: list of columns to use as Y axes. If None, all columns are used
except x column.
spacing: spacing between the plots
**kwargs: keyword arguments to pass to data.plot()
Returns:
a matplotlib.axes.Axes object returned from data.plot()
Example:
See Also:
This code is mentioned in https://stackoverflow.com/q/11640243/2593810
"""
# Get default color style from pandas - can be changed to any other color list
if y is None:
y = data.columns
# remove x_col from y_cols
if x:
y = [col for col in y if col != x]
if len(y) == 0:
return
colors = get_standard_colors(num_colors=len(y))
if "legend" not in kwargs:
kwargs["legend"] = False # prevent multiple legends
# First axis
ax = data.plot(x=x, y=y[0], color=colors[0], **kwargs)
ax.set_ylabel(ylabel=y[0])
lines, labels = ax.get_legend_handles_labels()
for i in range(1, len(y)):
# Multiple y-axes
ax_new = ax.twinx()
ax_new.spines["right"].set_position(("axes", 1 + spacing * (i - 1)))
data.plot(
ax=ax_new, x=x, y=y[i], color=colors[i % len(colors)], **kwargs
)
ax_new.set_ylabel(ylabel=y[i])
# Proper legend position
line, label = ax_new.get_legend_handles_labels()
lines += line
labels += label
ax.legend(lines, labels, loc=0)
return ax
def parse_logs(self, model_name: str, for_ui: bool = False):
"""Convert local TensorBoard data into Pandas DataFrame.
Function takes the root directory path and recursively parses
all events data.
If the `sort_by` value is provided then it will use that column
to sort values; typically `wall_time` or `step`.
*Note* that the whole data is converted into a DataFrame.
Depending on the data size this might take a while. If it takes
too long then narrow it to some sub-directories.
Paramters:
model_name: (str) path to db model config/dir.
for_ui: (bool) Generate UI-formatted text outputs.
Returns:
pandas.DataFrame with [wall_time, name, step, value] columns.
"""
matplotlib.use("Agg")
if for_ui:
print("Generating graphs?")
status.textinfo = "Generating graphs"
def convert_tfevent(filepath) -> Tuple[DataFrame, DataFrame, DataFrame, bool]:
loss_events = []
lr_events = []
ram_events = []
instance_loss_events = []
prior_loss_events = []
has_all = False
try:
import tensorflow
except:
print("Unable to import tensorflow")
return pd.DataFrame(loss_events), pd.DataFrame(lr_events), pd.DataFrame(ram_events), has_all
serialized_examples = tensorflow.data.TFRecordDataset(filepath)
for serialized_example in serialized_examples:
e = event_pb2.Event.FromString(serialized_example.numpy())
if len(e.summary.value):
parsed = parse_tfevent(e)
if parsed["Name"] == "lr":
lr_events.append(parsed)
elif parsed["Name"] == "loss":
loss_events.append(parsed)
elif parsed["Name"] == "vram_usage" or parsed["Name"] == "vram":
ram_events.append(parsed)
elif parsed["Name"] == "instance_loss" or parsed["Name"] == "inst_loss":
instance_loss_events.append(parsed)
elif parsed["Name"] == "prior_loss":
prior_loss_events.append(parsed)
merged_events = []
has_all = True
for le in loss_events:
lr = next((item for item in lr_events if item["Step"] == le["Step"]), None)
instance_loss = next((item for item in instance_loss_events if item["Step"] == le["Step"]), None)
prior_loss = next((item for item in prior_loss_events if item["Step"] == le["Step"]), None)
if lr is not None and instance_loss is not None and prior_loss is not None:
le["LR"] = lr["Value"]
le["Loss"] = le["Value"]
le["Instance_Loss"] = instance_loss["Value"]
le["Prior_Loss"] = prior_loss["Value"]
merged_events.append(le)
else:
has_all = False
if has_all:
loss_events = merged_events
return pd.DataFrame(loss_events), pd.DataFrame(lr_events), pd.DataFrame(ram_events), has_all
def parse_tfevent(tfevent):
return {
"Wall_time": tfevent.wall_time,
"Name": tfevent.summary.value[0].tag,
"Step": tfevent.step,
"Value": float(tfevent.summary.value[0].simple_value),
}
try:
from dreambooth.dataclasses.db_config import from_file # noqa
except:
from core.modules.dreambooth.dreambooth.dataclasses.db_config import from_file # noqa
model_config = from_file(model_name)
print(f"Model name: {model_name}")
if model_config is None:
print("Unable to load model config!")
return None
self.smoothing_window = int(model_config.graph_smoothing)
if self.model_name != model_name:
if for_ui:
print(f"Setting model name: {self.model_name}")
self.reset()
self.model_name = model_name
self.logging_dir = os.path.join(model_config.model_dir, "logging", "dreambooth")
columns_order = ['Wall_time', 'Name', 'Step', 'Value']
if for_ui:
print(f"Walking: {self.logging_dir}")
for (root, _, filenames) in os.walk(self.logging_dir):
for filename in filenames:
if "events.out.tfevents" not in filename and "dreambooth.events" not in filename:
continue
file_full_path = os.path.join(root, filename)
f_time = os.path.getmtime(file_full_path)
do_parse = True
if file_full_path in self.parsed_files.keys():
e_time = self.parsed_files[file_full_path]
if e_time != f_time:
print(f"Log file updated, re-parsing: {file_full_path}")
else:
print(f"Log file unchanged, nothing to do: {file_full_path}")
do_parse = False
if do_parse:
self.parsed_files[file_full_path] = f_time
converted_loss, converted_lr, converted_ram, merged = convert_tfevent(file_full_path)
self.parsed[file_full_path] = ParsedValues(converted_loss, converted_lr, converted_ram, merged)
out_loss = []
out_lr = []
out_ram = []
has_all_lr = True
for file, data in self.parsed.items():
out_loss.append(data.loss)
out_lr.append(data.lr)
out_ram.append(data.ram)
if not data.merged:
has_all_lr = False
loss_columns = columns_order
if has_all_lr:
loss_columns = ['Wall_time', 'Name', 'Step', 'Loss', "LR", "Instance_Loss", "Prior_Loss"]
# Concatenate (and sort) all partial individual dataframes
all_df_loss = pd.concat(out_loss)[loss_columns]
all_df_loss = all_df_loss.fillna(method="ffill")
all_df_loss = all_df_loss.sort_values("Wall_time")
all_df_loss = all_df_loss.reset_index(drop=True)
sw = int(self.smoothing_window if self.smoothing_window < len(all_df_loss) / 3 else len(all_df_loss) / 3)
all_df_loss = all_df_loss.rolling(sw).mean(numeric_only=True)
out_images = []
out_names = []
status.job_count = 2
status.job_no = 1
status.textinfo = "Plotting data..."
if has_all_lr:
plotted_loss = self.plot_multi_alt(
all_df_loss,
plot_definition=PlotDefinition(
title=f"Loss Average/Learning Rate ({model_config.lr_scheduler})",
x_axis="Step",
y_axis=[
YAxis(name="LR", columns=["LR"]),
YAxis(name="Loss", columns=["Instance_Loss", "Prior_Loss", "Loss"]),
]
)
)
loss_name = "Loss Average/Learning Rate"
else:
plotted_loss = all_df_loss.plot(x="Step", y="Value", title="Loss Averages")
loss_name = "Loss Averages"
all_df_lr = pd.concat(out_lr)[columns_order]
all_df_lr = all_df_lr.sort_values("Wall_time")
all_df_lr = all_df_lr.reset_index(drop=True)
all_df_lr = all_df_lr.rolling(self.smoothing_window).mean(numeric_only=True)
plotted_lr = all_df_lr.plot(x="Step", y="Value", title="Learning Rate")
lr_img = os.path.join(model_config.model_dir, "logging", f"lr_plot_{model_config.revision}.png")
plotted_lr.figure.savefig(lr_img)
matplotlib.pyplot.close(plotted_lr.figure)
log_lr = Image.open(lr_img)
out_images.append(log_lr)
out_names.append("Learning Rate")
status.job_no = 2
status.textinfo = "Saving graph data..."
loss_img = os.path.join(model_config.model_dir, "logging", f"loss_plot_{model_config.revision}.png")
print(f"Saving {loss_img}")
plotted_loss.figure.savefig(loss_img)
matplotlib.pyplot.close(plotted_loss.figure)
log_pil = Image.open(loss_img)
out_images.append(log_pil)
out_names.append(loss_name)
try:
all_df_ram = pd.concat(out_ram)[columns_order]
all_df_ram = all_df_ram.sort_values("Wall_time")
all_df_ram = all_df_ram.reset_index(drop=True)
all_df_ram = all_df_ram.rolling(self.smoothing_window).mean(numeric_only=True)
plotted_ram = all_df_ram.plot(x="Step", y="Value", title="VRAM Usage")
ram_img = os.path.join(model_config.model_dir, "logging", f"ram_plot_{model_config.revision}.png")
print(f"Saving {ram_img}")
plotted_ram.figure.savefig(ram_img)
matplotlib.pyplot.close(plotted_ram.figure)
out_images.append(ram_img)
out_names.append("VRAM Usage")
if for_ui:
out_names = "<br>".join(out_names)
except:
pass
del out_loss
del out_lr
del out_ram
try:
matplotlib.pyplot.close()
except:
pass
print("Cleanup log parse.")
return out_images, out_names
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