这是indexloc提供的服务，不要输入任何密码
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Fix unused imports #3

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Oct 3, 2017
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Fix unused imports #3

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6 changes: 1 addition & 5 deletions infer.py
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Original file line number Diff line number Diff line change
@@ -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')

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2 changes: 0 additions & 2 deletions reco_encoder/__init__.py
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@@ -1,3 +1 @@
# Copyright (c) 2017 NVIDIA Corporation
from . import data
from . import model
1 change: 0 additions & 1 deletion reco_encoder/data/__init__.py
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@@ -1,2 +1 @@
# Copyright (c) 2017 NVIDIA Corporation
from . import input_layer
2 changes: 0 additions & 2 deletions reco_encoder/model/__init__.py
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@@ -1,3 +1 @@
# Copyright (c) 2017 NVIDIA Corporation
from .model import AutoEncoder
from .model import MSEloss
6 changes: 0 additions & 6 deletions test/context.py
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7 changes: 3 additions & 4 deletions test/data_layer_tests.py
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@@ -1,23 +1,22 @@
# 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):
print("Test 1 started")
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)

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)
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33 changes: 16 additions & 17 deletions test/test_model.py
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Original file line number Diff line number Diff line change
@@ -1,25 +1,24 @@
# 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):
print("iRecAutoEncoderTest Test on CPU started")
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)
Expand All @@ -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()
Expand All @@ -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)
Expand All @@ -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()
Expand All @@ -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()
Expand All @@ -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)
Expand All @@ -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()
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