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_inception_e(system_dict):
forward = True
test = "test_block_inception_e"
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_e_block(pool_type="avg"))
network.append(gtf.inception_e_block(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_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, 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_multimargin(system_dict):
forward = True
test = "test_loss_multimargin"
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, dtype=torch.long).random_(5)
y = torch.randn(1, 5)
gtf.loss_multimargin()
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_multilabelmargin(system_dict):
forward = True;
test = "test_loss_multilabelmargin";
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.tensor([[3, 0, -1, 1, 2]])
y = torch.randn(1, 5);
gtf.loss_multilabel_margin();
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_layer_convolution1d(system_dict):
forward = True
test = "test_layer_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.convolution1d(output_channels=3, kernel_size=3))
gtf.Compile_Network(network, data_shape=(3, 128), use_gpu=False)
x = torch.randn(1, 3, 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_loss_binary_crossentropy(system_dict):
forward = True;
test = "test_loss_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);
m = torch.nn.Sigmoid();
y = m(y);
gtf.loss_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_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_optimizer_adagrad(system_dict):
forward = True;
test = "test_optimizer_adagrad";
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="../../system_check_tests/datasets/dataset_cats_dogs_train",
model_name="resnet18", freeze_base_network=True, num_epochs=2);
gtf.optimizer_adagrad(0.01, weight_decay=0.0001, learning_rate_decay=0.001);
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 = torch.randn(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_logsoftmax(system_dict):
forward = True;
test = "test_activation_logsoftmax";
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.logsoftmax());
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_optimizer_adam(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_adam";
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="../../system_check_tests/datasets/dataset_cats_dogs_train",
model_name="resnet18", freeze_base_network=True, num_epochs=2);
gtf.optimizer_adam(0.01, weight_decay=0.0001, beta1=0.9, beta2=0.999, amsgrad=True);
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_optimizer_adam(system_dict):
forward = True
test = "test_optimizer_adam"
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=
"../../system_check_tests/datasets/dataset_cats_dogs_train",
model_name="resnet18",
freeze_base_network=True,
num_epochs=2)
gtf.optimizer_adam(0.01,
weight_decay=0.0001,
beta1=0.9,
beta2=0.999,
amsgrad=True)
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_update_normal(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 = "update_normal_object_creation";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf = prototype(verbose=0);
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");
test = "update_normal_Prototype()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-3");
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");
test = "update_normal_Default()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Default(dataset_path="datasets/dataset_csv_id/train",
path_to_csv="datasets/dataset_csv_id/train.csv", delimiter=",",
model_name="resnet18", freeze_base_network=True, num_epochs=10);
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");
test = "update_normal_update_model_name()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_model_name("resnet50");
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");
test = "update_normal_update_use_gpu()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_use_gpu(False);
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");
test = "update_normal_update_use_pretrained()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_use_pretrained(True);
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");
test = "update_normal_update_freeze_base_network()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_freeze_base_network(False);
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");
test = "update_normal_update_freeze_layers()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_freeze_layers(10);
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");
test = "update_normal_update_num_epochs()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_num_epochs(2);
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");
test = "update_normal_update_display_progress_realtime()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_display_progress_realtime(False);
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");
test = "update_normal_update_display_progress()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_display_progress(False);
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");
test = "update_normal_update_save_intermediate_models()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_save_intermediate_models(False);
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");
test = "update_normal_update_save_training_logs()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.update_save_training_logs(True);
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");
test = "update_normal_lr_fixed()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.lr_fixed();
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");
test = "update_normal_Reload()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Reload();
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");
test = "update_normal_EDA()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.EDA(check_missing=True, check_corrupt=True);
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");
test = "update_normal_Estimate_Train_Time()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Estimate_Train_Time();
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");
test = "update_normal_Train()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.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_expert_eval_infer(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 = "expert_eval_infer_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "expert_eval_infer_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1",
"sample-experiment-4",
eval_infer=True)
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")
test = "expert_eval_infer_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(dataset_path="datasets/dataset_csv_id/test",
path_to_csv="datasets/dataset_csv_id/test.csv",
delimiter=",")
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")
test = "expert_eval_infer_reset_transforms()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.reset_transforms(test=True)
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")
test = "expert_eval_infer_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "expert_eval_infer_Evaluate()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate()
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_update_copy_from(system_dict):
forward = True
test = "update_copy_from_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "update_copy_from_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype(
"sample-project-1",
"sample-experiment-2",
copy_from=["sample-project-1", "sample-experiment-1"])
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")
test = "update_copy_from_reset_transforms()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.reset_transforms()
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")
test = "update_copy_from_apply_transforms()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.apply_random_resized_crop(256, train=True, val=True, test=True)
ptf.apply_random_perspective(train=True, val=True)
ptf.apply_random_vertical_flip(train=True, val=True)
ptf.apply_random_horizontal_flip(train=True, val=True)
ptf.apply_normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
train=True,
val=True,
test=True)
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")
test = "update_copy_from_update_dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_dataset(dataset_path=[
"../datasets/dataset_cats_dogs_train",
"../datasets/dataset_cats_dogs_eval"
])
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")
test = "update_copy_from_update_input_size()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_input_size(256)
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")
test = "update_copy_from_update_batch_size()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_batch_size(6)
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")
test = "update_copy_from_update_shuffle_data()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_shuffle_data(False)
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")
test = "update_copy_from_update_num_processors()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_num_processors(16)
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")
test = "update_copy_from_update_trainval_split()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_trainval_split(0.6)
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")
test = "update_copy_from_Reload()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Reload()
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")
test = "update_copy_from_EDA()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.EDA(check_missing=True, check_corrupt=True)
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")
test = "update_copy_from_Train()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.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_update_normal(system_dict):
forward = True
test = "update_normal_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "update_normal_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-3")
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")
test = "update_normal_Default()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Default(dataset_path="../datasets/dataset_csv_id/train",
path_to_csv="../datasets/dataset_csv_id/train.csv",
delimiter=",",
model_name="resnet18",
freeze_base_network=True,
num_epochs=10)
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")
test = "update_normal_update_model_name()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_model_name("resnet50")
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")
test = "update_normal_update_use_gpu()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_use_gpu(False)
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")
test = "update_normal_update_use_pretrained()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_use_pretrained(True)
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")
test = "update_normal_update_freeze_base_network()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_freeze_base_network(False)
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")
test = "update_normal_update_freeze_layers()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_freeze_layers(10)
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")
test = "update_normal_update_num_epochs()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_num_epochs(2)
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")
test = "update_normal_update_display_progress_realtime()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_display_progress_realtime(False)
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")
test = "update_normal_update_display_progress()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_display_progress(False)
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")
test = "update_normal_update_save_intermediate_models()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_save_intermediate_models(False)
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")
test = "update_normal_update_save_training_logs()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_save_training_logs(True)
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")
test = "update_normal_lr_fixed()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.lr_fixed()
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")
test = "update_normal_Reload()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Reload()
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")
test = "update_normal_EDA()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.EDA(check_missing=True, check_corrupt=True)
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")
test = "update_normal_Estimate_Train_Time()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Estimate_Train_Time()
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")
test = "update_normal_Train()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.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_switch_default(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 = "switch_default_object_object_creation";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf = prototype(verbose=0);
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");
test = "switch_default_object_Prototype()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-5");
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");
test = "switch_default_Default()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Default(dataset_path=["datasets/dataset_cats_dogs_train", "datasets/dataset_cats_dogs_eval"],
model_name="resnet18", freeze_base_network=True, num_epochs=10);
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");
test = "switch_default_EDA()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.EDA(check_missing=True, check_corrupt=True);
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");
test = "switch_default_Switch_Mode()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Switch_Mode(eval_infer=True);
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");
test = "switch_default_Dataset_Params()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset_Params(dataset_path="datasets/dataset_cats_dogs_eval");
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");
test = "switch_default_Dataset()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset();
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");
test = "switch_default_Evaluate()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate();
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");
test = "switch_default_Switch_Mode()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Switch_Mode(train=True);
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");
test = "switch_default_Train()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.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
import sys
sys.path.append("../../../../monk_v1/");
sys.path.append("../../../monk/");
import psutil
from pytorch_prototype import prototype
from compare_prototype import compare
from common import print_start
from common import print_status
import torch
import numpy as np
from pytorch.losses.return_loss import load_loss
gtf = prototype(verbose=0);
gtf.Prototype("sample-project-1", "sample-experiment-1");
network = [];
network.append(gtf.transposed_convolution3d(output_channels=3, kernel_size=3));
gtf.Compile_Network(network, data_shape=(3, 3, 64, 64), use_gpu=False);
x = torch.randn(1, 3, 3, 64, 64);
y = gtf.system_dict["local"]["model"](x);
def test_layer_transposed_convolution3d(system_dict):
forward = True;
test = "test_layer_transposed_convolution3d";
def test_expert_train(system_dict):
forward = True
test = "expert_train_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "expert_train_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-4")
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")
test = "expert_train_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(dataset_path=[
"../datasets/dataset_csv_id/train",
"../datasets/dataset_csv_id/val"
],
path_to_csv=[
"../datasets/dataset_csv_id/train.csv",
"../datasets/dataset_csv_id/val.csv"
],
split=0.9,
input_size=224,
batch_size=16,
shuffle_data=True,
num_processors=3)
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")
test = "expert_train_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "expert_train_Model_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model_Params(model_name="resnet18",
freeze_base_network=True,
use_gpu=True,
use_pretrained=True)
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")
test = "expert_train_append_layer()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.append_dropout(probability=0.1)
ptf.append_linear(final_layer=True)
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")
test = "expert_train_Model()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model()
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")
test = "expert_train_lr_step_decrease()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.lr_step_decrease(1, gamma=0.9)
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")
test = "expert_train_optimizer_sgd()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.optimizer_sgd(0.001, momentum=0.9)
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")
test = "expert_train_loss_softmax_crossentropy()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.loss_softmax_crossentropy()
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")
test = "expert_train_Training_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Training_Params(
num_epochs=3,
display_progress=True,
display_progress_realtime=True,
save_intermediate_models=True,
intermediate_model_prefix="intermediate_model_",
save_training_logs=True)
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")
test = "expert_train_Train()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.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_expert_eval_infer(system_dict):
forward = True
test = "expert_eval_infer_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "expert_eval_infer_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1",
"sample-experiment-4",
eval_infer=True)
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")
test = "expert_eval_infer_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(
dataset_path="../datasets/dataset_csv_id/test",
path_to_csv="../datasets/dataset_csv_id/test.csv",
delimiter=",")
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")
test = "expert_eval_infer_reset_transforms()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.reset_transforms(test=True)
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")
test = "expert_eval_infer_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "expert_eval_infer_Evaluate()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate()
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_switch_expert(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 = "switch_expert_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "switch_expert_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-6")
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")
test = "switch_expert_switch_mode()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Switch_Mode(eval_infer=True)
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")
test = "switch_expert_Model_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model_Params(
model_path=
"workspace/sample-project-1/sample-experiment-5/output/models/intermediate_model_9",
use_gpu=True)
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")
test = "switch_expert_Model()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model()
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")
test = "switch_expert_update_input_size()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_input_size(224)
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")
test = "switch_expert_Infer-Img()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
img_name = "datasets/dataset_cats_dogs_test/0.jpg"
predictions = ptf.Infer(img_name=img_name, return_raw=True)
img_name = "datasets/dataset_cats_dogs_test/84.jpg"
predictions = ptf.Infer(img_name=img_name, return_raw=True)
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")
test = "switch_expert_Infer-Folder()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
inference_dataset = "datasets/dataset_cats_dogs_test/"
output = ptf.Infer(img_dir=inference_dataset, return_raw=True)
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")
test = "switch_expert_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(dataset_path="datasets/dataset_cats_dogs_eval",
input_size=224)
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")
test = "switch_expert_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "switch_expert_Evaluate()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate()
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")
test = "switch_expert_switch_mode()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Switch_Mode(train=True)
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")
test = "expert_train_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(dataset_path="datasets/dataset_cats_dogs_train",
split=0.9,
input_size=224,
batch_size=16,
shuffle_data=True,
num_processors=3)
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")
test = "expert_train_apply_transforms()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.apply_random_resized_crop(256, train=True, val=True, test=True)
ptf.apply_random_perspective(train=True, val=True)
ptf.apply_random_vertical_flip(train=True, val=True)
ptf.apply_random_horizontal_flip(train=True, val=True)
ptf.apply_normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
train=True,
val=True,
test=True)
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")
test = "expert_train_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "expert_train_Model_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model_Params(model_name="resnet18",
freeze_base_network=True,
use_gpu=True,
use_pretrained=True)
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")
test = "expert_train_Model()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Model()
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")
test = "expert_train_lr_multistep_decrease()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.lr_multistep_decrease([1, 3], gamma=0.9)
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")
test = "expert_train_optimizer_sgd()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.optimizer_sgd(0.001, momentum=0.9)
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")
test = "expert_train_loss_softmax_crossentropy()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.loss_softmax_crossentropy()
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")
test = "expert_train_Training_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Training_Params(
num_epochs=4,
display_progress=True,
display_progress_realtime=True,
save_intermediate_models=True,
intermediate_model_prefix="intermediate_model_",
save_training_logs=True)
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")
test = "expert_train_Train()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.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_switch_default(system_dict):
forward = True;
test = "switch_default_object_object_creation";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf = prototype(verbose=0);
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");
test = "switch_default_object_Prototype()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-5");
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");
test = "switch_default_Default()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Default(dataset_path=["../datasets/dataset_cats_dogs_train", "../datasets/dataset_cats_dogs_eval"],
model_name="resnet18", freeze_base_network=True, num_epochs=10);
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");
test = "switch_default_EDA()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.EDA(check_missing=True, check_corrupt=True);
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");
test = "switch_default_Switch_Mode()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Switch_Mode(eval_infer=True);
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");
test = "switch_default_Dataset_Params()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset_Params(dataset_path="../datasets/dataset_cats_dogs_eval");
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");
test = "switch_default_Dataset()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset();
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");
test = "switch_default_Evaluate()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate();
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");
test = "switch_default_Switch_Mode()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Switch_Mode(train=True);
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");
test = "switch_default_Train()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.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_expert_train(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 = "expert_train_object_creation";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf = prototype(verbose=0);
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");
test = "expert_train_Prototype()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-4");
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");
test = "expert_train_Dataset_Params()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset_Params(dataset_path=["datasets/dataset_csv_id/train", "datasets/dataset_csv_id/val"],
path_to_csv=["datasets/dataset_csv_id/train.csv", "datasets/dataset_csv_id/val.csv"],
split=0.9, input_size=224, batch_size=16, shuffle_data=True, num_processors=3);
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");
test = "expert_train_Dataset()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Dataset();
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");
test = "expert_train_Model_Params()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Model_Params(model_name="resnet18", freeze_base_network=True, use_gpu=True, use_pretrained=True);
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");
test = "expert_train_append_layer()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.append_dropout(probability=0.1);
ptf.append_linear(final_layer=True);
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");
test = "expert_train_Model()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Model();
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");
test = "expert_train_lr_step_decrease()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.lr_step_decrease(1, gamma=0.9);
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");
test = "expert_train_optimizer_sgd()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.optimizer_sgd(0.001, momentum=0.9);
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");
test = "expert_train_loss_softmax_crossentropy()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.loss_softmax_crossentropy();
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");
test = "expert_train_Training_Params()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Training_Params(num_epochs=3, display_progress=True, display_progress_realtime=True,
save_intermediate_models=True, intermediate_model_prefix="intermediate_model_", save_training_logs=True);
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");
test = "expert_train_Train()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.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 Analyse_Optimizers(self, analysis_name, optimizer_list, percent_data, num_epochs=2, state="keep_all"):
from pytorch_prototype import prototype
project = analysis_name;
self.custom_print("");
self.custom_print("Running Optimizer analysis"); #Change 1
self.custom_print("Analysis Name : {}".format(project));
self.custom_print("");
for i in range(len(optimizer_list)): #Change 2
ptf_ = prototype(verbose=0);
self.custom_print("Running experiment : {}/{}".format(i+1, len(optimizer_list))); #Change 3
experiment = "Optimizer_" + str(optimizer_list[i]); #Change 4, 5
self.custom_print("Experiment name : {}".format(experiment))
ptf_.Prototype(project, experiment, pseudo_copy_from=[self.system_dict["project_name"], self.system_dict["experiment_name"]]);
ptf_.Dataset_Percent(percent_data);
dataset_type = ptf_.system_dict["dataset"]["dataset_type"];
dataset_train_path = ptf_.system_dict["dataset"]["train_path"];
dataset_val_path = ptf_.system_dict["dataset"]["val_path"];
csv_train = ptf_.system_dict["dataset"]["csv_train"];
csv_val = ptf_.system_dict["dataset"]["csv_val"];
if(dataset_type=="train"):
ptf_.update_dataset(dataset_path=dataset_train_path, path_to_csv="sampled_dataset_train.csv");
elif(dataset_type=="train-val"):
ptf_.update_dataset(dataset_path=[dataset_train_path, dataset_val_path],
path_to_csv=["sampled_dataset_train.csv", "sampled_dataset_val.csv"]);
elif(dataset_type=="csv_train"):
ptf_.update_dataset(dataset_path=dataset_train_path, path_to_csv="sampled_dataset_train.csv");
elif(dataset_type=="csv_train-val"):
ptf_.update_dataset(dataset_path=[dataset_train_path, dataset_val_path],
path_to_csv=["sampled_dataset_train.csv", "sampled_dataset_val.csv"]);
lr = ptf_.system_dict["hyper-parameters"]["learning_rate"]
if(optimizer_list[i] == "adagrad"): #Change 6
ptf_.optimizer_adagrad(lr);
elif(optimizer_list[i] == "adadelta"):
ptf_.optimizer_adadelta(lr);
elif(optimizer_list[i] == "adam"):
ptf_.optimizer_adam(lr);
elif(optimizer_list[i] == "adamw"):
ptf_.optimizer_adamw(lr);
elif(optimizer_list[i] == "sparseadam"):
ptf_.optimizer_sparseadam(lr);
elif(optimizer_list[i] == "adamax"):
ptf_.optimizer_adamax(lr);
elif(optimizer_list[i] == "asgd"):
ptf_.optimizer_asgd(lr);
elif(optimizer_list[i] == "rmsprop"):
ptf_.optimizer_rmsprop(lr);
elif(optimizer_list[i] == "rprop"):
ptf_.optimizer_rprop(lr);
elif(optimizer_list[i] == "sgd"):
ptf_.optimizer_sgd(lr);
ptf_.Reload(); #Change 7
ptf_.update_num_epochs(num_epochs);
ptf_.update_display_progress_realtime(False)
ptf_.update_save_intermediate_models(False);
total_time_per_epoch = ptf_.get_training_estimate();
total_time = total_time_per_epoch*num_epochs;
if(int(total_time//60) == 0):
self.custom_print("Estimated time : {} sec".format(total_time));
else:
self.custom_print("Estimated time : {} min".format(int(total_time//60)+1));
ptf_.Train();
self.custom_print("Experiment Complete");
self.custom_print("\n");
self.custom_print("Comparing Experiments");
from compare_prototype import compare
ctf_ = compare(verbose=0);
ctf_.Comparison("Comparison_" + analysis_name);
self.custom_print("Comparison ID: {}".format("Comparison_" + analysis_name));
training_accuracies = [];
validation_accuracies = [];
training_losses = [];
validation_losses = [];
tabular_data = [];
for i in range(len(optimizer_list)): #Change 8
project = analysis_name;
experiment = "Optimizer_" + str(optimizer_list[i]); #Change 9, 10
ctf_.Add_Experiment(project, experiment)
tmp = [];
tmp.append(experiment);
training_accuracy_file = self.system_dict["master_systems_dir_relative"] + "/" + project + "/" + experiment + "/output/logs/train_acc_history.npy";
tmp.append(np.load(training_accuracy_file, allow_pickle=True)[-1]);
validation_accuracy_file = self.system_dict["master_systems_dir_relative"] + "/" + project + "/" + experiment + "/output/logs/val_acc_history.npy";
tmp.append(np.load(validation_accuracy_file, allow_pickle=True)[-1]);
training_loss_file = self.system_dict["master_systems_dir_relative"] + "/" + project + "/" + experiment + "/output/logs/train_loss_history.npy";
tmp.append(np.load(training_loss_file, allow_pickle=True)[-1]);
validation_loss_file = self.system_dict["master_systems_dir_relative"] + "/" + project + "/" + experiment + "/output/logs/val_loss_history.npy";
tmp.append(np.load(validation_loss_file, allow_pickle=True)[-1]);
tabular_data.append(tmp)
ctf_.Generate_Statistics();
self.custom_print("Generated statistics post all epochs");
self.custom_print(tabulate(tabular_data, headers=['Experiment Name', 'Train Acc', 'Val Acc', 'Train Loss', 'Val Loss'], tablefmt='orgtbl'));
self.custom_print("");
return_dict = {};
for i in range(len(tabular_data)):
return_dict[tabular_data[i][0]] = {};
return_dict[tabular_data[i][0]]["training_accuracy"] = tabular_data[i][1];
return_dict[tabular_data[i][0]]["validation_accuracy"] = tabular_data[i][2];
return_dict[tabular_data[i][0]]["training_loss"] = tabular_data[i][3];
return_dict[tabular_data[i][0]]["validation_loss"] = tabular_data[i][4];
fname = self.system_dict["master_systems_dir_relative"] + analysis_name + "/" + tabular_data[i][0] + "/experiment_state.json";
system_dict = read_json(fname);
return_dict[tabular_data[i][0]]["training_time"] = system_dict["training"]["outputs"]["training_time"];
if(state=="keep_none"):
shutil.rmtree(self.system_dict["master_systems_dir_relative"] + analysis_name);
return return_dict
###############################################################################################################################################
import os
import sys
sys.path.append("../../../monk/")
import psutil
from pytorch_prototype import prototype
ptf = prototype(verbose=1)
ptf.Prototype("sample-project-1", "sample-experiment-1")
ptf.Default(
dataset_path=
"../../../monk/system_check_tests/datasets/dataset_cats_dogs_train",
model_name="resnet18",
freeze_base_network=True,
num_epochs=2)
################################################### Dataset Updates ######################################################################
ptf.update_input_size(256)
ptf.update_batch_size(6)
ptf.update_shuffle_data(True)
ptf.update_num_processors(psutil.cpu_count())
ptf.update_trainval_split(0.6)
ptf.update_dataset(dataset_path=[
"../../../monk/system_check_tests/datasets/dataset_cats_dogs_train",
"../../../monk/system_check_tests/datasets/dataset_cats_dogs_eval"
])
#################################################################################################################################################
################################################### Transforms Updates ######################################################################
# Reset Transforms if required
def test_analyse(system_dict):
forward = True
test = "analyse_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "analyse_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-1")
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")
test = "analyse_Default()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Default(dataset_path="../datasets/dataset_cats_dogs_train",
model_name="resnet18",
freeze_base_network=True,
num_epochs=2)
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")
test = "analyse_Analyse_Learning_Rates()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
analysis_name = "analyse_learning_rates"
lrs = [0.1, 0.05]
epochs = 2
percent_data = 40
analysis = ptf.Analyse_Learning_Rates(analysis_name,
lrs,
percent_data,
num_epochs=epochs,
state="keep_none")
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")
test = "analyse_Analyse_Input_Sizes()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
analysis_name = "analyse_input_sizes"
input_sizes = [128, 256]
epochs = 2
percent_data = 40
analysis = ptf.Analyse_Input_Sizes(analysis_name,
input_sizes,
percent_data,
num_epochs=epochs,
state="keep_none")
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")
test = "analyse_Analyse_Batch_Sizes()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
analysis_name = "analyse_batch_sizes"
batch_sizes = [2, 3]
epochs = 2
percent_data = 40
analysis = ptf.Analyse_Batch_Sizes(analysis_name,
batch_sizes,
percent_data,
num_epochs=epochs,
state="keep_none")
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")
test = "analyse_Analyse_Models()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
analysis_name = "analyse_models"
models = [["resnet18", True, True], ["resnet34", False, True]]
percent_data = 40
analysis = ptf.Analyse_Models(analysis_name,
models,
percent_data,
num_epochs=epochs,
state="keep_none")
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")
test = "analyse_Analyse_Optimizers()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
analysis_name = "analyse_optimizers"
optimizers = ["sgd", "adam"]
epochs = 2
percent_data = 40
analysis = ptf.Analyse_Optimizers(analysis_name,
optimizers,
percent_data,
num_epochs=epochs,
state="keep_none")
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_analyse(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 = "analyse_object_creation";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf = prototype(verbose=0);
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");
test = "analyse_Prototype()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Prototype("sample-project-1", "sample-experiment-1");
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");
test = "analyse_Default()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
ptf.Default(dataset_path="datasets/dataset_cats_dogs_train",
model_name="resnet18", freeze_base_network=True, num_epochs=2);
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");
test = "analyse_Analyse_Learning_Rates()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
analysis_name = "analyse_learning_rates"
lrs = [0.1, 0.05];
epochs=2
percent_data=40
analysis = ptf.Analyse_Learning_Rates(analysis_name, lrs, percent_data, num_epochs=epochs, state="keep_none");
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");
test = "analyse_Analyse_Input_Sizes()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
analysis_name = "analyse_input_sizes";
input_sizes = [128, 256];
epochs=2;
percent_data=40;
analysis = ptf.Analyse_Input_Sizes(analysis_name, input_sizes, percent_data, num_epochs=epochs, state="keep_none");
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");
test = "analyse_Analyse_Batch_Sizes()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
analysis_name = "analyse_batch_sizes";
batch_sizes = [2, 3];
epochs = 2;
percent_data = 40;
analysis = ptf.Analyse_Batch_Sizes(analysis_name, batch_sizes, percent_data, num_epochs=epochs, state="keep_none");
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");
test = "analyse_Analyse_Models()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
analysis_name = "analyse_models";
models = [["resnet18", True, True], ["resnet34", False, True]];
percent_data=40;
analysis = ptf.Analyse_Models(analysis_name, models, percent_data, num_epochs=epochs, state="keep_none");
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");
test = "analyse_Analyse_Optimizers()";
system_dict["total_tests"] += 1;
print_start(test, system_dict["total_tests"])
if(forward):
try:
analysis_name = "analyse_optimizers";
optimizers = ["sgd", "adam"];
epochs = 2;
percent_data = 40;
analysis = ptf.Analyse_Optimizers(analysis_name, optimizers, percent_data, num_epochs=epochs, state="keep_none");
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
def test_update_eval_infer(system_dict):
forward = True
test = "update_normal_object_creation"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf = prototype(verbose=0)
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")
test = "update_normal_Prototype()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Prototype("sample-project-1",
"sample-experiment-3",
eval_infer=True)
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")
test = "update_normal_Dataset_Params()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset_Params(
dataset_path="../datasets/dataset_csv_id/test",
path_to_csv="../datasets/dataset_csv_id/test.csv",
delimiter=",")
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")
test = "default_eval_infer_Dataset()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Dataset()
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")
test = "update_normal_update_model_path()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.update_model_path(
"workspace/sample-project-1/sample-experiment-3/output/models/best_model"
)
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")
test = "update_normal_Reload()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
ptf.Reload()
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")
test = "default_eval_infer_Evaluate()"
system_dict["total_tests"] += 1
print_start(test, system_dict["total_tests"])
if (forward):
try:
accuracy, class_based_accuracy = ptf.Evaluate()
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
