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LTH_fig1_large.py
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LTH_fig1_large.py
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import torch
from torch import nn
import torchvision
import matplotlib.pyplot as plt
from d2l import torch as d2l
from dl_assignment_7_common import * # Your functions should go here if you want to use them from scripts
import os
import numpy as np
import torch.nn.utils.prune as prune
print('start')
run_name = 'large'
cp_path = f'./checkpoints/{run_name}'
if not os.path.exists(cp_path):
os.makedirs(cp_path)
result_path = f'./results/{run_name}'
if not os.path.exists(result_path):
os.makedirs(result_path)
device = d2l.try_gpu()
dataset_used = get_dataset('mnist', dir = './data', batch_size = 60, shuffle = True, download = False)
L1_size, random_size = 5, 10
remaining_percentages = np.array([100,90,80,70,60,50,40,30,20,10])
prune_fractions = (100-remaining_percentages)/100
epochs_random = [ 10,10,10,10,10,10,10,20,20,20]
epochs_L1 = [ 10,10,10,10,10,10,10,20,20,20]
early_stop_iterations_lenet_L1 = np.zeros([len(prune_fractions),L1_size])
early_stop_iterations_lenet_L1 = np.insert(early_stop_iterations_lenet_L1, 0, prune_fractions, axis=1)
early_stop_iterations_lenet_random = np.zeros([len(prune_fractions),random_size])
early_stop_iterations_lenet_random = np.insert(early_stop_iterations_lenet_random, 0, prune_fractions, axis=1)
early_stop_testacc_lenet_L1 = np.zeros([len(prune_fractions),L1_size])
early_stop_testacc_lenet_L1 = np.insert(early_stop_testacc_lenet_L1, 0, prune_fractions, axis=1)
early_stop_testacc_lenet_random = np.zeros([len(prune_fractions),random_size])
early_stop_testacc_lenet_random = np.insert(early_stop_testacc_lenet_random, 0, prune_fractions, axis=1)
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
_, early_stop_values = train(net, optimizer, dataset_used, epochs = 50, file_specifier = f'LTH_fig1_base', val_interval = 2, cp_path = cp_path ,plot = False)
for j in range(L1_size):
for i, fraction in enumerate(prune_fractions):
print(f'run {j} for fraction {fraction} L1 pruned')
trained_net = torch.load(f"{cp_path}/model_LeNet-after-LTH_fig1_base.pth")
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
net.load_state_dict(trained_net.state_dict())
mask = L1_prune(net, fraction)
init_net = torch.load(f"{cp_path}/model_LeNet-before-LTH_fig1_base.pth")
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
net.load_state_dict(init_net.state_dict())
init_net_pruned = prune_using_mask(net, mask)
optimizer = torch.optim.Adam(init_net_pruned.parameters(), lr=0.0012)
_, early_stop_values = train(init_net_pruned, optimizer, dataset_used, epochs = epochs_L1[i], file_specifier = f'LTH_L1_pruned{fraction}', val_interval = 2, cp_path=cp_path , plot = False)
early_stop_iterations_lenet_L1[i,j+1] = early_stop_values['iteration']
early_stop_testacc_lenet_L1[i,j+1] = early_stop_values['test_acc']
np.savetxt(f'{result_path}/early_stop_iterations_lenet_L1_{run_name}.txt',early_stop_iterations_lenet_L1)
np.savetxt(f'{result_path}/early_stop_testacc_lenet_L1_{run_name}.txt',early_stop_testacc_lenet_L1)
print('start random')
for j in range(random_size):
for i, fraction in enumerate(prune_fractions):
print(f'run {j} for fraction {fraction} random pruned')
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
_, early_stop_values = train(net, optimizer, dataset_used, epochs = 10, file_specifier = f'LTH_fig1_base', val_interval = 2, cp_path=cp_path , plot = False)
trained_net = torch.load(f"{cp_path}/model_LeNet-after-LTH_fig1_base.pth")
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
net.state_dict(trained_net.state_dict())
mask = random_prune(net, fraction)
init_net = torch.load(f"{cp_path}/model_LeNet-before-LTH_fig1_base.pth")
net, optimizer = create_network(arch = 'LeNet', input = 784, output = 10)
net.state_dict(init_net.state_dict())
init_net_pruned = prune_using_mask(net, mask)
optimizer = torch.optim.Adam(init_net_pruned.parameters(), lr=0.0012)
_, early_stop_values = train(init_net_pruned, optimizer, dataset_used, epochs = epochs_L1[i], file_specifier = f'LTH_random_pruned{fraction}', val_interval = 2, cp_path=cp_path , plot = False)
early_stop_iterations_lenet_random[i,j+1] = early_stop_values['iteration']
early_stop_testacc_lenet_random[i,j+1] = early_stop_values['test_acc']
np.savetxt(f'{result_path}/early_stop_iterations_lenet_random_{run_name}.txt',early_stop_iterations_lenet_random)
np.savetxt(f'{result_path}/early_stop_testacc_lenet_random_{run_name}.txt',early_stop_testacc_lenet_random)
# # Equally spaced x values for plotting
# x_spaced = np.arange(len(prune_fractions))
# plt.figure(figsize=(10, 6))
# plt.errorbar(x_spaced,early_stop_iterations_lenet_random_avg,yerr=np.std(early_stop_iterations_lenet_random[:,1:],axis=1),label='random')
# plt.errorbar(x_spaced,early_stop_iterations_lenet_L1_avg,yerr=np.std(early_stop_iterations_lenet_L1[:,1:],axis=1),label='L1')
# plt.xticks(x_spaced, prune_fractions)
# plt.xlabel('Percent of Weights Remaining')
# plt.ylabel('EarlyStop Iteration (Val)')
# plt.legend()
# plt.grid(True)
# plt.tight_layout()
# plt.savefig('./results/fig1.png')
# plt.show()
# plt.figure(figsize=(10, 6))
# plt.errorbar(x_spaced,early_stop_testacc_lenet_random_avg,yerr=np.std(early_stop_testacc_lenet_random[:,1:],axis=1),label='random')
# plt.errorbar(x_spaced,early_stop_testacc_lenet_L1_avg,yerr=np.std(early_stop_testacc_lenet_L1[:,1:],axis=1),label='L1')
# plt.xticks(x_spaced, prune_fractions)
# plt.xlabel('Percent of Weights Remaining')
# plt.ylabel('Accuracy at Early-Stop (Test)')
# plt.legend()
# plt.grid(True)
# plt.tight_layout()
# plt.savefig('./results/fig2.png')
# plt.show()