def test_activation_tanh(system_dict):
forward = True
test = "test_activation_tanh"
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.tanh())
gtf.Compile_Network(network)
x = np.random.rand(1, 64, 4)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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_squeezenet_fire(system_dict):
forward = True
test = "test_block_squeezenet_fire"
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.squeezenet_fire_block(squeeze_channels=16,
expand_channels_1x1=32,
expand_channels_3x3=64))
gtf.Compile_Network(network, use_gpu=False)
x = np.random.rand(1, 1, 64, 64)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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_global_average_pooling3d(system_dict):
forward = True;
test = "test_layer_average_pooling3d";
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.global_average_pooling3d());
gtf.Compile_Network(network, use_gpu=False);
x = np.random.rand(1, 1, 10, 64, 64);
x = mx.nd.array(x);
y = gtf.system_dict["local"]["model"].forward(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_convolution1d(system_dict):
forward = True
test = "test_layer_transposed_convolution1d"
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_convolution1d(output_channels=3, kernel_size=3))
gtf.Compile_Network(network, use_gpu=False)
x = np.random.rand(1, 64, 3)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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_poisson_nll(system_dict):
forward = True
test = "test_loss_poisson_nll"
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 = np.random.rand(1, 5)
label = mx.nd.array(label)
y = np.random.rand(1, 5)
y = mx.nd.array(y)
gtf.loss_poisson_nll()
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_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 = [1, 0, 1, 0, 1];
label = mx.nd.array(label);
y = np.random.rand(1, 5);
y = mx.nd.array(y);
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_optimizer_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_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_v1", freeze_base_network=True, num_epochs=2);
gtf.optimizer_rmsprop(0.01, weight_decay=0.0001, decay_rate=0.9,
clipnorm=1.0, clipvalue=0.5);
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_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, use_gpu=False)
x = np.random.rand(1, 1, 64, 64)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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_mobilenet_v2_linear_bottleneck(system_dict):
forward = True
test = "test_block_mobilenet_v2_linear_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.mobilenet_v2_linear_bottleneck_block(output_channels=64,
bottleneck_width=4,
stride=1))
gtf.Compile_Network(network, use_gpu=False)
x = np.random.rand(1, 64, 64, 64)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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_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, use_gpu=False);
x = np.random.rand(1, 1, 64, 64);
x = mx.nd.array(x);
y = gtf.system_dict["local"]["model"].forward(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_initializer_xavier_normal(system_dict):
forward = True
test = "test_initializer_xavier_normal"
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, uid="conv1"))
network.append(gtf.batch_normalization(uid="bn1"))
network.append(gtf.relu(uid="relu1"))
network.append(gtf.convolution(output_channels=16, uid="conv2"))
network.append(gtf.batch_normalization(uid="bn2"))
network.append(gtf.relu(uid="relu2"))
network.append(gtf.max_pooling(uid="pool1"))
network.append(gtf.flatten(uid="flatten1"))
network.append(gtf.fully_connected(units=1024, uid="fc1"))
network.append(gtf.dropout(drop_probability=0.2, uid="dp1"))
network.append(gtf.fully_connected(units=2, uid="fc2"))
gtf.Compile_Network(network,
use_gpu=False,
network_initializer="xavier_normal")
x = np.random.rand(1, 1, 64, 64)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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, uid="conv1"))
network.append(gtf.batch_normalization(uid="bn1"))
network.append(gtf.relu(uid="relu1"))
network.append(gtf.max_pooling(uid="pool1"))
subnetwork = []
branch1 = []
branch1.append(gtf.convolution(output_channels=16,
uid="conv3_1_1"))
branch1.append(gtf.batch_normalization(uid="bn3_1_1"))
branch1.append(gtf.convolution(output_channels=16,
uid="conv3_1_2"))
branch1.append(gtf.batch_normalization(uid="bn3_1_2"))
branch2 = []
branch2.append(gtf.convolution(output_channels=16,
uid="conv3_2_1"))
branch2.append(gtf.batch_normalization(uid="bn3_2_1"))
branch3 = []
branch3.append(gtf.identity(uid="identity1"))
subnetwork.append(branch1)
subnetwork.append(branch2)
subnetwork.append(branch3)
subnetwork.append(gtf.concatenate(uid="concat1"))
network.append(subnetwork)
network.append(gtf.convolution(output_channels=16, uid="conv4"))
network.append(gtf.batch_normalization(uid="bn4"))
network.append(gtf.relu(uid="relu3"))
network.append(gtf.max_pooling(uid="pool4"))
network.append(gtf.flatten(uid="flatten1"))
network.append(gtf.fully_connected(units=1024, uid="fc1"))
network.append(gtf.dropout(drop_probability=0.2, uid="dp1"))
network.append(gtf.fully_connected(units=2, uid="fc2"))
gtf.Compile_Network(network, use_gpu=False)
x = np.random.rand(1, 1, 64, 64)
x = mx.nd.array(x)
y = gtf.system_dict["local"]["model"].forward(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
