From 8846149191b29195f27a7061d2948f4210e1d667 Mon Sep 17 00:00:00 2001 From: Dat Le Date: Tue, 3 Oct 2017 08:14:22 +0800 Subject: [PATCH] Fix unused imports --- infer.py | 6 +----- reco_encoder/__init__.py | 2 -- reco_encoder/data/__init__.py | 1 - reco_encoder/model/__init__.py | 2 -- test/context.py | 6 ------ test/data_layer_tests.py | 7 +++---- test/test_model.py | 33 ++++++++++++++++----------------- 7 files changed, 20 insertions(+), 37 deletions(-) delete mode 100644 test/context.py diff --git a/infer.py b/infer.py index 53caede..2eb0f4a 100644 --- a/infer.py +++ b/infer.py @@ -1,15 +1,11 @@ # Copyright (c) 2017 NVIDIA Corporation import torch import argparse +import copy from reco_encoder.data import input_layer from reco_encoder.model import model -import torch.optim as optim -import torch.nn as nn from torch.autograd import Variable -import copy -import time from pathlib import Path -import numpy as np parser = argparse.ArgumentParser(description='RecoEncoder') diff --git a/reco_encoder/__init__.py b/reco_encoder/__init__.py index 68d1463..bad4325 100644 --- a/reco_encoder/__init__.py +++ b/reco_encoder/__init__.py @@ -1,3 +1 @@ # Copyright (c) 2017 NVIDIA Corporation -from . import data -from . import model \ No newline at end of file diff --git a/reco_encoder/data/__init__.py b/reco_encoder/data/__init__.py index c61fef8..bad4325 100644 --- a/reco_encoder/data/__init__.py +++ b/reco_encoder/data/__init__.py @@ -1,2 +1 @@ # Copyright (c) 2017 NVIDIA Corporation -from . import input_layer \ No newline at end of file diff --git a/reco_encoder/model/__init__.py b/reco_encoder/model/__init__.py index 2932d87..bad4325 100644 --- a/reco_encoder/model/__init__.py +++ b/reco_encoder/model/__init__.py @@ -1,3 +1 @@ # Copyright (c) 2017 NVIDIA Corporation -from .model import AutoEncoder -from .model import MSEloss \ No newline at end of file diff --git a/test/context.py b/test/context.py deleted file mode 100644 index 856808b..0000000 --- a/test/context.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) 2017 NVIDIA Corporation -import os -import sys -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -import reco_encoder \ No newline at end of file diff --git a/test/data_layer_tests.py b/test/data_layer_tests.py index 1421ed3..a83d25e 100644 --- a/test/data_layer_tests.py +++ b/test/data_layer_tests.py @@ -1,7 +1,6 @@ # Copyright (c) 2017 NVIDIA Corporation import unittest -import sys -from .context import reco_encoder +from reco_encoder.data.input_layer import UserItemRecDataProvider class UserItemRecDataProviderTest(unittest.TestCase): def test_1(self): @@ -9,7 +8,7 @@ def test_1(self): params = {} params['batch_size'] = 64 params['data_dir'] = 'test/testData_iRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Total items found: {}".format(len(data_layer.data.keys()))) self.assertTrue(len(data_layer.data.keys())>0) @@ -17,7 +16,7 @@ def test_iterations(self): params = {} params['batch_size'] = 32 params['data_dir'] = 'test/testData_iRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Total items found: {}".format(len(data_layer.data.keys()))) for i, data in enumerate(data_layer.iterate_one_epoch()): print(i) diff --git a/test/test_model.py b/test/test_model.py index 60168d9..6f54f5f 100644 --- a/test/test_model.py +++ b/test/test_model.py @@ -1,13 +1,12 @@ # Copyright (c) 2017 NVIDIA Corporation import unittest import sys -sys.path.append('data') -sys.path.append('model') -import torch -from .context import reco_encoder import torch.optim as optim -import torch.nn as nn from torch.autograd import Variable +from reco_encoder.data.input_layer import UserItemRecDataProvider +from reco_encoder.model.model import AutoEncoder, MSEloss +sys.path.append('data') +sys.path.append('model') class iRecAutoEncoderTest(unittest.TestCase): def test_CPU(self): @@ -15,11 +14,11 @@ def test_CPU(self): params = {} params['batch_size'] = 64 params['data_dir'] = 'test/testData_iRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Vector dim: {}".format(data_layer.vector_dim)) print("Total items found: {}".format(len(data_layer.data.keys()))) self.assertTrue(len(data_layer.data.keys())>0) - encoder = reco_encoder.model.AutoEncoder(layer_sizes=[data_layer.vector_dim, 256, 128], is_constrained=True) + encoder = AutoEncoder(layer_sizes=[data_layer.vector_dim, 256, 128], is_constrained=True) print(encoder) print(encoder.parameters()) optimizer = optim.SGD(encoder.parameters(), lr=0.01, momentum=0.9) @@ -28,7 +27,7 @@ def test_CPU(self): inputs = Variable(mb.to_dense()) optimizer.zero_grad() outputs = encoder(inputs) - loss, num_ratings = reco_encoder.model.MSEloss(outputs, inputs) + loss, num_ratings = MSEloss(outputs, inputs) loss = loss / num_ratings loss.backward() optimizer.step() @@ -39,10 +38,10 @@ def test_GPU(self): params = {} params['batch_size'] = 32 params['data_dir'] = 'test/testData_iRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Total items found: {}".format(len(data_layer.data.keys()))) self.assertTrue(len(data_layer.data.keys()) > 0) - encoder = reco_encoder.model.AutoEncoder(layer_sizes=[data_layer.vector_dim, 1024, 512, 512, 512, 512, 128]) + encoder = AutoEncoder(layer_sizes=[data_layer.vector_dim, 1024, 512, 512, 512, 512, 128]) encoder.cuda() optimizer = optim.Adam(encoder.parameters()) print(encoder) @@ -53,7 +52,7 @@ def test_GPU(self): inputs = Variable(mb.to_dense().cuda()) optimizer.zero_grad() outputs = encoder(inputs) - loss, num_ratings = reco_encoder.model.MSEloss(outputs, inputs) + loss, num_ratings = MSEloss(outputs, inputs) loss = loss / num_ratings loss.backward() optimizer.step() @@ -67,18 +66,18 @@ def test_CPU(self): params = {} params['batch_size'] = 256 params['data_dir'] = 'test/testData_uRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Vector dim: {}".format(data_layer.vector_dim)) print("Total items found: {}".format(len(data_layer.data.keys()))) self.assertTrue(len(data_layer.data.keys())>0) - encoder = reco_encoder.model.AutoEncoder(layer_sizes=[data_layer.vector_dim, 128, data_layer.vector_dim]) + encoder = AutoEncoder(layer_sizes=[data_layer.vector_dim, 128, data_layer.vector_dim]) optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9) for epoch in range(1): for i, mb in enumerate(data_layer.iterate_one_epoch()): inputs = Variable(mb.to_dense()) optimizer.zero_grad() outputs = encoder(inputs) - loss, num_ratings = reco_encoder.model.MSEloss(outputs, inputs) + loss, num_ratings = MSEloss(outputs, inputs) loss = loss / num_ratings loss.backward() optimizer.step() @@ -91,10 +90,10 @@ def test_GPU(self): params = {} params['batch_size'] = 64 params['data_dir'] = 'test/testData_uRec' - data_layer = reco_encoder.data.input_layer.UserItemRecDataProvider(params=params) + data_layer = UserItemRecDataProvider(params=params) print("Total items found: {}".format(len(data_layer.data.keys()))) self.assertTrue(len(data_layer.data.keys()) > 0) - encoder = reco_encoder.model.AutoEncoder(layer_sizes=[data_layer.vector_dim, 1024, 512, 512, 128]) + encoder = AutoEncoder(layer_sizes=[data_layer.vector_dim, 1024, 512, 512, 128]) encoder.cuda() optimizer = optim.Adam(encoder.parameters()) print(encoder) @@ -105,7 +104,7 @@ def test_GPU(self): inputs = Variable(mb.to_dense().cuda()) optimizer.zero_grad() outputs = encoder(inputs) - loss, num_ratings = reco_encoder.model.MSEloss(outputs, inputs) + loss, num_ratings = MSEloss(outputs, inputs) loss = loss / num_ratings loss.backward() optimizer.step()