refactoring and running experiments
This commit is contained in:
Steffen Illium
@@ -1,12 +1,5 @@
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from pathlib import Path
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import torch
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import torchmetrics
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from experiments.meta_task_utility import set_checkpoint
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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@@ -24,31 +17,6 @@ class AddTaskDataset(Dataset):
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return ab, ab.sum(axis=-1, keepdims=True)
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def validate(checkpoint_path, valid_d, ratio=1, validmetric=torchmetrics.MeanAbsoluteError()):
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checkpoint_path = Path(checkpoint_path)
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# initialize metric
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model = torch.load(checkpoint_path, map_location=DEVICE).eval()
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n_samples = int(len(valid_d) * ratio)
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with tqdm(total=n_samples, desc='Validation Run: ') as pbar:
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for idx, (valid_batch_x, valid_batch_y) in enumerate(valid_d):
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valid_batch_x, valid_batch_y = valid_batch_x.to(DEVICE), valid_batch_y.to(DEVICE)
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y_valid = model(valid_batch_x)
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# metric on current batch
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acc = validmetric(y_valid.cpu(), valid_batch_y.cpu())
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pbar.set_postfix_str(f'Acc: {acc}')
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pbar.update()
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if idx == n_samples:
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break
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# metric on all batches using custom accumulation
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acc = validmetric.compute()
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tqdm.write(f"Avg. Accuracy on all data: {acc}")
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return acc
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def train_task(model, optimizer, loss_func, btch_x, btch_y) -> (dict, torch.Tensor):
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# Zero your gradients for every batch!
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optimizer.zero_grad()
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@@ -66,12 +34,5 @@ def train_task(model, optimizer, loss_func, btch_x, btch_y) -> (dict, torch.Tens
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return stp_log, y_prd
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def checkpoint_and_validate(model, out_path, epoch_n, valid_d, final_model=False):
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out_path = Path(out_path)
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ckpt_path = set_checkpoint(model, out_path, epoch_n, final_model=final_model)
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result = validate(ckpt_path, valid_d)
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return result
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if __name__ == '__main__':
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raise(NotImplementedError('Get out of here'))
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@@ -3,57 +3,30 @@ import re
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import shutil
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from collections import defaultdict
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from pathlib import Path
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import sys
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import platform
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import pandas as pd
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import numpy as np
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import torch
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import torchmetrics
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from matplotlib import pyplot as plt
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import seaborn as sns
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from torch.nn import Flatten
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from torch.utils.data import Dataset, DataLoader
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from torchvision.datasets import MNIST
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from torchvision.transforms import ToTensor, Compose, Resize
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from torch.utils.data import Dataset
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from tqdm import tqdm
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# noinspection DuplicatedCode
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if platform.node() == 'CarbonX':
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debug = True
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print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
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print("@ Warning, Debugging Config@!!!!!! @")
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print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
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else:
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debug = False
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try:
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# noinspection PyUnboundLocalVariable
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if __package__ is None:
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DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(DIR.parent))
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__package__ = DIR.name
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else:
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DIR = None
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except NameError:
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DIR = None
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pass
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from network import FixTypes as ft
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from functionalities_test import test_for_fixpoints
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WORKER = 10 if not debug else 0
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debug = False
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BATCHSIZE = 500 if not debug else 50
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WORKER = 10
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BATCHSIZE = 500
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EPOCH = 50
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VALIDATION_FRQ = 3 if not debug else 1
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SELF_TRAIN_FRQ = 1 if not debug else 1
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VALIDATION_FRQ = 3
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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DATA_PATH = Path('data')
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DATA_PATH.mkdir(exist_ok=True, parents=True)
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if debug:
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torch.autograd.set_detect_anomaly(True)
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class ToFloat:
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@@ -93,39 +66,28 @@ def set_checkpoint(model, out_path, epoch_n, final_model=False):
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return ckpt_path
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def validate(checkpoint_path, ratio=0.1):
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# noinspection PyProtectedMember
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def validate(checkpoint_path, valid_loader, metric_class=torchmetrics.Accuracy):
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checkpoint_path = Path(checkpoint_path)
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import torchmetrics
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# initialize metric
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validmetric = torchmetrics.Accuracy()
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ut = Compose([ToTensor(), ToFloat(), Resize((15, 15)), Flatten(start_dim=0)])
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try:
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datas = MNIST(str(DATA_PATH), transform=ut, train=False)
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except RuntimeError:
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datas = MNIST(str(DATA_PATH), transform=ut, train=False, download=True)
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valid_d = DataLoader(datas, batch_size=BATCHSIZE, shuffle=True, drop_last=True, num_workers=WORKER)
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validmetric = metric_class()
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model = torch.load(checkpoint_path, map_location=DEVICE).eval()
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n_samples = int(len(valid_d) * ratio)
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with tqdm(total=n_samples, desc='Validation Run: ') as pbar:
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for idx, (valid_batch_x, valid_batch_y) in enumerate(valid_d):
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with tqdm(total=len(valid_loader), desc='Validation Run: ') as pbar:
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for idx, (valid_batch_x, valid_batch_y) in enumerate(valid_loader):
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valid_batch_x, valid_batch_y = valid_batch_x.to(DEVICE), valid_batch_y.to(DEVICE)
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y_valid = model(valid_batch_x)
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# metric on current batch
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acc = validmetric(y_valid.cpu(), valid_batch_y.cpu())
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pbar.set_postfix_str(f'Acc: {acc}')
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measure = validmetric(y_valid.cpu(), valid_batch_y.cpu())
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pbar.set_postfix_str(f'Measure: {measure}')
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pbar.update()
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if idx == n_samples:
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break
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# metric on all batches using custom accumulation
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acc = validmetric.compute()
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tqdm.write(f"Avg. accuracy on all data: {acc}")
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return acc
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measure = validmetric.compute()
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tqdm.write(f"Avg. {validmetric._get_name()} on all data: {measure}")
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return measure
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def new_storage_df(identifier, weight_count):
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@@ -135,14 +97,16 @@ def new_storage_df(identifier, weight_count):
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return pd.DataFrame(columns=['Epoch', 'Weight', *(f'weight_{x}' for x in range(weight_count))])
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def checkpoint_and_validate(model, out_path, epoch_n, final_model=False):
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def checkpoint_and_validate(model, valid_loader, out_path, epoch_n, final_model=False,
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validation_metric=torchmetrics.Accuracy):
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out_path = Path(out_path)
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ckpt_path = set_checkpoint(model, out_path, epoch_n, final_model=final_model)
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result = validate(ckpt_path)
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result = validate(ckpt_path, valid_loader, metric_class=validation_metric)
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return result
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def plot_training_particle_types(path_to_dataframe):
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plt.close('all')
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plt.clf()
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# load from Drive
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df = pd.read_csv(path_to_dataframe, index_col=False)
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@@ -158,17 +122,19 @@ def plot_training_particle_types(path_to_dataframe):
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fig.legend(loc="center right", title='Particle Type', bbox_to_anchor=(0.85, 0.5))
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plt.tight_layout()
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if debug:
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plt.show()
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else:
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plt.savefig(Path(path_to_dataframe.parent / 'training_particle_type_lp.png'), dpi=300)
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plt.savefig(Path(path_to_dataframe.parent / 'training_particle_type_lp.png'), dpi=300)
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def plot_training_result(path_to_dataframe):
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def plot_training_result(path_to_dataframe, metric='Accuracy', plot_name=None):
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plt.clf()
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# load from Drive
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df = pd.read_csv(path_to_dataframe, index_col=False)
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# Check if this is a single lineplot or if aggregated
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group = ['Epoch', 'Metric']
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if 'Seed' in df.columns:
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group.append('Seed')
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# Set up figure
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fig, ax1 = plt.subplots() # initializes figure and plots
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ax2 = ax1.twinx() # applies twinx to ax2, which is the second y-axis.
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@@ -176,26 +142,27 @@ def plot_training_result(path_to_dataframe):
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# plots the first set of data
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data = df[(df['Metric'] == 'Task Loss') | (df['Metric'] == 'Self Train Loss')].groupby(['Epoch', 'Metric']).mean()
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palette = sns.color_palette()[1:data.reset_index()['Metric'].unique().shape[0]+1]
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sns.lineplot(data=data.groupby(['Epoch', 'Metric']).mean(), x='Epoch', y='Score', hue='Metric',
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palette=palette, ax=ax1)
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sns.lineplot(data=data.groupby(group).mean(), x='Epoch', y='Score', hue='Metric',
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palette=palette, ax=ax1, ci='sd')
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# plots the second set of data
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data = df[(df['Metric'] == 'Test Accuracy') | (df['Metric'] == 'Train Accuracy')]
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data = df[(df['Metric'] == f'Test {metric}') | (df['Metric'] == f'Train {metric}')]
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palette = sns.color_palette()[len(palette)+1:data.reset_index()['Metric'].unique().shape[0] + len(palette)+1]
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sns.lineplot(data=data, x='Epoch', y='Score', marker='o', hue='Metric', palette=palette)
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sns.lineplot(data=data, x='Epoch', y='Score', marker='o', hue='Metric', palette=palette, ci='sd')
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ax1.set(yscale='log', ylabel='Losses')
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ax1.set_title('Training Lineplot')
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ax2.set(ylabel='Accuracy')
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# ax1.set_title('Training Lineplot')
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ax2.set(ylabel=metric)
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if metric != 'MAE':
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ax2.set(yscale='log')
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fig.legend(loc="center right", title='Metric', bbox_to_anchor=(0.85, 0.5))
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ax1.get_legend().remove()
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ax2.get_legend().remove()
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for ax in [ax1, ax2]:
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if legend := ax.get_legend():
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legend.remove()
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plt.tight_layout()
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if debug:
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plt.show()
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else:
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plt.savefig(Path(path_to_dataframe.parent / 'training_lineplot.png'), dpi=300)
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plt.savefig(Path(path_to_dataframe.parent / ('training_lineplot.png' if plot_name is None else plot_name)), dpi=300)
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def plot_network_connectivity_by_fixtype(path_to_trained_model):
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@@ -224,31 +191,29 @@ def plot_network_connectivity_by_fixtype(path_to_trained_model):
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lines = ax.get_lines()
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for line in lines:
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line.set_color(sns.color_palette()[n])
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if debug:
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plt.show()
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else:
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plt.savefig(Path(path_to_trained_model.parent / f'net_connectivity_{fixtype}.png'), dpi=300)
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plt.savefig(Path(path_to_trained_model.parent / f'net_connectivity_{fixtype}.png'), dpi=300)
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tqdm.write(f'Connectivity plottet: {fixtype} - n = {df[df["type"] == fixtype].shape[0] // 2}')
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else:
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tqdm.write(f'No Connectivity {fixtype}')
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def run_particle_dropout_test(model_path):
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# noinspection PyProtectedMember
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def run_particle_dropout_test(model_path, valid_loader, metric_class=torchmetrics.Accuracy):
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diff_store_path = model_path.parent / 'diff_store.csv'
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latest_model = torch.load(model_path, map_location=DEVICE).eval()
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prtcl_dict = defaultdict(lambda: 0)
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_ = test_for_fixpoints(prtcl_dict, list(latest_model.particles))
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tqdm.write(str(dict(prtcl_dict)))
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diff_df = pd.DataFrame(columns=['Particle Type', 'Accuracy', 'Diff'])
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diff_df = pd.DataFrame(columns=['Particle Type', metric_class()._get_name(), 'Diff'])
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acc_pre = validate(model_path, ratio=1).item()
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acc_pre = validate(model_path, valid_loader, metric_class=metric_class).item()
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diff_df.loc[diff_df.shape[0]] = ('All Organism', acc_pre, 0)
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for fixpoint_type in ft.all_types():
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new_model = torch.load(model_path, map_location=DEVICE).eval().replace_with_zero(fixpoint_type)
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if [x for x in new_model.particles if x.is_fixpoint == fixpoint_type]:
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new_ckpt = set_checkpoint(new_model, model_path.parent, fixpoint_type, final_model=True)
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acc_post = validate(new_ckpt, ratio=1).item()
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acc_post = validate(new_ckpt, valid_loader, metric_class=metric_class).item()
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acc_diff = abs(acc_post - acc_pre)
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tqdm.write(f'Zero_ident diff = {acc_diff}')
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diff_df.loc[diff_df.shape[0]] = (fixpoint_type, acc_post, acc_diff)
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@@ -257,8 +222,9 @@ def run_particle_dropout_test(model_path):
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return diff_store_path
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def plot_dropout_stacked_barplot(mdl_path, diff_store_path):
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# noinspection PyProtectedMember
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def plot_dropout_stacked_barplot(mdl_path, diff_store_path, metric_class=torchmetrics.Accuracy):
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metric_name = metric_class()._get_name()
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diff_df = pd.read_csv(diff_store_path)
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particle_dict = defaultdict(lambda: 0)
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latest_model = torch.load(mdl_path, map_location=DEVICE).eval()
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@@ -267,24 +233,21 @@ def plot_dropout_stacked_barplot(mdl_path, diff_store_path):
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plt.clf()
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fig, ax = plt.subplots(ncols=2)
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colors = sns.color_palette()[1:diff_df.shape[0]+1]
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_ = sns.barplot(data=diff_df, y='Accuracy', x='Particle Type', ax=ax[0], palette=colors)
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_ = sns.barplot(data=diff_df, y=metric_name, x='Particle Type', ax=ax[0], palette=colors)
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ax[0].set_title('Accuracy after particle dropout')
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ax[0].set_title(f'{metric_name} after particle dropout')
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ax[0].set_xlabel('Particle Type')
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ax[1].pie(particle_dict.values(), labels=particle_dict.keys(), colors=list(reversed(colors)), )
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ax[1].set_title('Particle Count')
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plt.tight_layout()
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if debug:
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plt.show()
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else:
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plt.savefig(Path(diff_store_path.parent / 'dropout_stacked_barplot.png'), dpi=300)
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plt.savefig(Path(diff_store_path.parent / 'dropout_stacked_barplot.png'), dpi=300)
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def run_particle_dropout_and_plot(model_path):
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diff_store_path = run_particle_dropout_test(model_path)
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plot_dropout_stacked_barplot(model_path, diff_store_path)
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def run_particle_dropout_and_plot(model_path, valid_loader, metric_class=torchmetrics.Accuracy):
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diff_store_path = run_particle_dropout_test(model_path, valid_loader=valid_loader, metric_class=metric_class)
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plot_dropout_stacked_barplot(model_path, diff_store_path, metric_class=metric_class)
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def flat_for_store(parameters):
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