def test_layer_global_max_pooling2d(system_dict):
forward = True
test = "test_layer_global_max_pooling2d"
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_max_pooling2d())
gtf.Compile_Network(network, data_shape=(3, 32, 32), use_gpu=False)
x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
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_optimizer_nesterov_sgd(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_nesterov_sgd";
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="resnet50", freeze_base_network=True, num_epochs=2);
gtf.optimizer_nesterov_sgd(0.01, momentum=0.9, weight_decay=0.0001, momentum_dampening_rate=0,
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_mobilenet_v2_inverted_linear_bottleneck(system_dict):
forward = True;
test = "test_block_mobilenet_v2_inverted_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_inverted_linear_bottleneck_block(output_channels=64, bottleneck_width=4, stride=1));
gtf.Compile_Network(network, data_shape=(64, 64, 64), use_gpu=False);
x = tf.placeholder(tf.float32, shape=(1, 64, 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_sigmoid(system_dict):
forward = True;
test = "test_activation_sigmoid";
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.sigmoid());
gtf.Compile_Network(network, data_shape=(3, 32, 32), use_gpu=False);
x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
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_squared_hinge(system_dict):
forward = True
test = "test_loss_squared_hinge"
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")
y = np.random.randn(1, 5)
label = np.random.randn(1, 5)
y = K.constant(y)
label = K.constant(label)
gtf.loss_squared_hinge()
load_loss(gtf.system_dict)
loss_obj = gtf.system_dict["local"]["criterion"]
loss_val = loss_obj(label, y)
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_conv_bn_relu(system_dict):
forward = True
test = "test_block_conv_bn_relu"
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.conv_bn_relu_block(output_channels=64))
gtf.Compile_Network(network, data_shape=(1, 64, 64), use_gpu=False)
x = tf.placeholder(tf.float32, shape=(1, 64, 64, 1))
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_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 = tf.placeholder(tf.float32, shape=(1, 64, 64, 1))
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_fully_connected(system_dict):
forward = True
test = "test_layer_fully_connected"
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.convolution2d(output_channels=3, kernel_size=3))
network.append(gtf.flatten())
network.append(gtf.fully_connected(units=10))
gtf.Compile_Network(network, data_shape=(3, 32, 32), use_gpu=False)
x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
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");
y = np.random.randn(1, 5);
label = np.random.randn(1, 5);
y = K.constant(y);
label = K.constant(label);
gtf.loss_kldiv();
load_loss(gtf.system_dict);
loss_obj = gtf.system_dict["local"]["criterion"];
loss_val = loss_obj(label, y);
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 = tf.placeholder(tf.float32, shape=(1, 64, 64, 1))
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_add(system_dict):
forward = True
test = "test_layer_add"
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.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.add())
network.append(subnetwork)
gtf.Compile_Network(network, data_shape=(3, 32, 32), use_gpu=False)
x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
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_initializer_he_uniform(system_dict):
forward = True;
test = "test_initializer_he_uniform";
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.convolution(output_channels=32));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.convolution(output_channels=64));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.convolution(output_channels=64));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.convolution(output_channels=128));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.convolution(output_channels=128));
network.append(gtf.batch_normalization());
network.append(gtf.relu());
network.append(gtf.average_pooling(kernel_size=2));
network.append(gtf.flatten());
network.append(gtf.dropout(drop_probability=0.2));
network.append(gtf.fully_connected(units=1024));
network.append(gtf.dropout(drop_probability=0.2));
network.append(gtf.fully_connected(units=2));
network.append(gtf.softmax());
gtf.Compile_Network(network, data_shape=(3, 32, 32), network_initializer="he_uniform");
x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
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
