def test_optimizer_momentum_rmsprop(system_dict):
forward = True;
if(not os.path.isdir("datasets")):
os.system("! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2\" -O datasets.zip && rm -rf /tmp/cookies.txt")
os.system("! unzip -qq datasets.zip")
test = "test_optimizer_momentum_rmsprop";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
gtf.Default(dataset_path="datasets/dataset_cats_dogs_train",
model_name="resnet18", freeze_base_network=True, num_epochs=2);
gtf.optimizer_momentum_rmsprop(0.01, weight_decay=0.0001, decay_rate=0.9);
gtf.Train();
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_activation_softmin(system_dict):
forward = True
test = "test_activation_softmin"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
gtf = prototype(verbose=0)
gtf.Prototype("sample-project-1", "sample-experiment-1")
network = []
network.append(gtf.softmin())
gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False)
x = torch.randn(1, 3, 64, 64)
y = gtf.system_dict["local"]["model"](x)
system_dict["successful_tests"] += 1
print_status("Pass")
except Exception as e:
system_dict["failed_tests_exceptions"].append(e)
system_dict["failed_tests_lists"].append(test)
forward = False
print_status("Fail")
else:
system_dict["skipped_tests_lists"].append(test)
print_status("Skipped")
return system_dict
def test_activation_rrelu(system_dict):
forward = True;
test = "test_activation_rrelu";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
network = [];
network.append(gtf.rrelu());
gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False);
x = torch.randn(1, 3, 64, 64);
y = gtf.system_dict["local"]["model"](x);
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_loss_kldiv(system_dict):
forward = True
test = "test_loss_kldiv"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
gtf = prototype(verbose=0)
gtf.Prototype("sample-project-1", "sample-experiment-1")
label = torch.randn(1, 5)
y = torch.randn(1, 5)
gtf.loss_kldiv()
load_loss(gtf.system_dict)
loss_obj = gtf.system_dict["local"]["criterion"]
loss_val = loss_obj(y, label)
system_dict["successful_tests"] += 1
print_status("Pass")
except Exception as e:
system_dict["failed_tests_exceptions"].append(e)
system_dict["failed_tests_lists"].append(test)
forward = False
print_status("Fail")
else:
system_dict["skipped_tests_lists"].append(test)
print_status("Skipped")
return system_dict
def test_block_densenet(system_dict):
forward = True;
test = "test_block_densenet";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
network = [];
network.append(gtf.densenet_block(bottleneck_size=4, growth_rate=16, dropout=0.2));
gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);
x = torch.randn(1, 1, 64, 64);
y = gtf.system_dict["local"]["model"](x);
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_loss_sigmoid_binary_crossentropy(system_dict):
forward = True;
test = "test_loss_sigmoid_binary_crossentropy";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
label = torch.empty((1, 5)).random_(2);
y = torch.randn(1, 5);
gtf.loss_sigmoid_binary_crossentropy();
load_loss(gtf.system_dict);
loss_obj = gtf.system_dict["local"]["criterion"];
loss_val = loss_obj(y, label);
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_block_inception_a(system_dict):
forward = True;
test = "test_block_inception_a";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
network = [];
network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="avg"));
network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="max"));
gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);
x = torch.randn(1, 1, 64, 64);
y = gtf.system_dict["local"]["model"](x);
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_layer_transposed_convolution2d(system_dict):
forward = True
test = "test_layer_transposed_convolution2d"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
gtf = prototype(verbose=0)
gtf.Prototype("sample-project-1", "sample-experiment-1")
network = []
network.append(
gtf.transposed_convolution2d(output_channels=3, kernel_size=3))
gtf.Compile_Network(network,
data_shape=(3, 128, 128),
use_gpu=False)
x = torch.randn(1, 3, 128, 128)
y = gtf.system_dict["local"]["model"](x)
system_dict["successful_tests"] += 1
print_status("Pass")
except Exception as e:
system_dict["failed_tests_exceptions"].append(e)
system_dict["failed_tests_lists"].append(test)
forward = False
print_status("Fail")
else:
system_dict["skipped_tests_lists"].append(test)
print_status("Skipped")
return system_dict
def test_block_resnet_v2_bottleneck(system_dict):
forward = True;
test = "test_block_resnet_v2_bottleneck";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
network = [];
network.append(gtf.resnet_v2_bottleneck_block(output_channels=32, stride=1, downsample=True));
network.append(gtf.resnet_v2_bottleneck_block(output_channels=32, stride=1, downsample=False));
gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False);
x = torch.randn(1, 1, 64, 64);
y = gtf.system_dict["local"]["model"](x);
system_dict["successful_tests"] += 1;
print_status("Pass");
except Exception as e:
system_dict["failed_tests_exceptions"].append(e);
system_dict["failed_tests_lists"].append(test);
forward = False;
print_status("Fail");
else:
system_dict["skipped_tests_lists"].append(test);
print_status("Skipped");
return system_dict
def test_block_resnext(system_dict):
forward = True
test = "test_block_resnext"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
gtf = prototype(verbose=0)
gtf.Prototype("sample-project-1", "sample-experiment-1")
network = []
network.append(
gtf.resnext_block(output_channels=256,
cardinality=8,
bottleneck_width=4,
stride=1,
downsample=True))
network.append(
gtf.resnext_block(output_channels=256,
cardinality=8,
bottleneck_width=4,
stride=1,
downsample=False))
gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False)
x = torch.randn(1, 1, 64, 64)
y = gtf.system_dict["local"]["model"](x)
system_dict["successful_tests"] += 1
print_status("Pass")
except Exception as e:
system_dict["failed_tests_exceptions"].append(e)
system_dict["failed_tests_lists"].append(test)
forward = False
print_status("Fail")
else:
system_dict["skipped_tests_lists"].append(test)
print_status("Skipped")
return system_dict
def test_layer_concatenate(system_dict):
forward = True
test = "test_layer_concatenate"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
gtf = prototype(verbose=0)
gtf.Prototype("sample-project-1", "sample-experiment-1")
network = []
network.append(gtf.convolution(output_channels=16))
network.append(gtf.batch_normalization())
network.append(gtf.relu())
network.append(gtf.max_pooling())
subnetwork = []
branch1 = []
branch1.append(gtf.convolution(output_channels=16))
branch1.append(gtf.batch_normalization())
branch1.append(gtf.convolution(output_channels=16))
branch1.append(gtf.batch_normalization())
branch2 = []
branch2.append(gtf.convolution(output_channels=16))
branch2.append(gtf.batch_normalization())
branch3 = []
branch3.append(gtf.identity())
subnetwork.append(branch1)
subnetwork.append(branch2)
subnetwork.append(branch3)
subnetwork.append(gtf.concatenate())
network.append(subnetwork)
network.append(gtf.convolution(output_channels=16))
network.append(gtf.batch_normalization())
network.append(gtf.relu())
network.append(gtf.max_pooling())
network.append(gtf.flatten())
network.append(gtf.fully_connected(units=1024))
network.append(gtf.dropout(drop_probability=0.2))
network.append(gtf.fully_connected(units=2))
gtf.Compile_Network(network, data_shape=(3, 64, 64), use_gpu=False)
x = torch.randn(1, 3, 64, 64)
y = gtf.system_dict["local"]["model"](x)
system_dict["successful_tests"] += 1
print_status("Pass")
except Exception as e:
system_dict["failed_tests_exceptions"].append(e)
system_dict["failed_tests_lists"].append(test)
forward = False
print_status("Fail")
else:
system_dict["skipped_tests_lists"].append(test)
print_status("Skipped")
return system_dict