def test_activation_tanh(system_dict): forward = True test = "test_activation_tanh" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append(gtf.tanh()) gtf.Compile_Network(network) x = np.random.rand(1, 64, 4) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_block_squeezenet_fire(system_dict): forward = True test = "test_block_squeezenet_fire" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append( gtf.squeezenet_fire_block(squeeze_channels=16, expand_channels_1x1=32, expand_channels_3x3=64)) gtf.Compile_Network(network, use_gpu=False) x = np.random.rand(1, 1, 64, 64) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_layer_global_average_pooling3d(system_dict): forward = True; test = "test_layer_average_pooling3d"; system_dict["total_tests"] += 1; print_start(test, system_dict["total_tests"]) if(forward): try: gtf = prototype(verbose=0); gtf.Prototype("sample-project-1", "sample-experiment-1"); network = []; network.append(gtf.global_average_pooling3d()); gtf.Compile_Network(network, use_gpu=False); x = np.random.rand(1, 1, 10, 64, 64); x = mx.nd.array(x); y = gtf.system_dict["local"]["model"].forward(x); system_dict["successful_tests"] += 1; print_status("Pass"); except Exception as e: system_dict["failed_tests_exceptions"].append(e); system_dict["failed_tests_lists"].append(test); forward = False; print_status("Fail"); else: system_dict["skipped_tests_lists"].append(test); print_status("Skipped"); return system_dict
def test_layer_transposed_convolution1d(system_dict): forward = True test = "test_layer_transposed_convolution1d" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append( gtf.transposed_convolution1d(output_channels=3, kernel_size=3)) gtf.Compile_Network(network, use_gpu=False) x = np.random.rand(1, 64, 3) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_loss_poisson_nll(system_dict): forward = True test = "test_loss_poisson_nll" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") label = np.random.rand(1, 5) label = mx.nd.array(label) y = np.random.rand(1, 5) y = mx.nd.array(y) gtf.loss_poisson_nll() load_loss(gtf.system_dict) loss_obj = gtf.system_dict["local"]["criterion"] loss_val = loss_obj(y, label) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_loss_kldiv(system_dict): forward = True; test = "test_loss_kldiv"; system_dict["total_tests"] += 1; print_start(test, system_dict["total_tests"]) if(forward): try: gtf = prototype(verbose=0); gtf.Prototype("sample-project-1", "sample-experiment-1"); label = [1, 0, 1, 0, 1]; label = mx.nd.array(label); y = np.random.rand(1, 5); y = mx.nd.array(y); gtf.loss_kldiv(); load_loss(gtf.system_dict); loss_obj = gtf.system_dict["local"]["criterion"]; loss_val = loss_obj(y, label); system_dict["successful_tests"] += 1; print_status("Pass"); except Exception as e: system_dict["failed_tests_exceptions"].append(e); system_dict["failed_tests_lists"].append(test); forward = False; print_status("Fail"); else: system_dict["skipped_tests_lists"].append(test); print_status("Skipped"); return system_dict
def test_optimizer_rmsprop(system_dict): forward = True; if(not os.path.isdir("datasets")): os.system("! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1rG-U1mS8hDU7_wM56a1kc-li_zHLtbq2\" -O datasets.zip && rm -rf /tmp/cookies.txt") os.system("! unzip -qq datasets.zip") test = "test_optimizer_rmsprop"; system_dict["total_tests"] += 1; print_start(test, system_dict["total_tests"]) if(forward): try: gtf = prototype(verbose=0); gtf.Prototype("sample-project-1", "sample-experiment-1"); gtf.Default(dataset_path="datasets/dataset_cats_dogs_train", model_name="resnet18_v1", freeze_base_network=True, num_epochs=2); gtf.optimizer_rmsprop(0.01, weight_decay=0.0001, decay_rate=0.9, clipnorm=1.0, clipvalue=0.5); gtf.Train(); system_dict["successful_tests"] += 1; print_status("Pass"); except Exception as e: system_dict["failed_tests_exceptions"].append(e); system_dict["failed_tests_lists"].append(test); forward = False; print_status("Fail"); else: system_dict["skipped_tests_lists"].append(test); print_status("Skipped"); return system_dict
def test_block_densenet(system_dict): forward = True test = "test_block_densenet" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append( gtf.densenet_block(bottleneck_size=4, growth_rate=16, dropout=0.2)) gtf.Compile_Network(network, use_gpu=False) x = np.random.rand(1, 1, 64, 64) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_block_mobilenet_v2_linear_bottleneck(system_dict): forward = True test = "test_block_mobilenet_v2_linear_bottleneck" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append( gtf.mobilenet_v2_linear_bottleneck_block(output_channels=64, bottleneck_width=4, stride=1)) gtf.Compile_Network(network, use_gpu=False) x = np.random.rand(1, 64, 64, 64) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_block_inception_a(system_dict): forward = True; test = "test_block_inception_a"; system_dict["total_tests"] += 1; print_start(test, system_dict["total_tests"]) if(forward): try: gtf = prototype(verbose=0); gtf.Prototype("sample-project-1", "sample-experiment-1"); network = []; network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="avg")); network.append(gtf.inception_a_block(pooling_branch_channels=32, pool_type="max")); gtf.Compile_Network(network, use_gpu=False); x = np.random.rand(1, 1, 64, 64); x = mx.nd.array(x); y = gtf.system_dict["local"]["model"].forward(x); system_dict["successful_tests"] += 1; print_status("Pass"); except Exception as e: system_dict["failed_tests_exceptions"].append(e); system_dict["failed_tests_lists"].append(test); forward = False; print_status("Fail"); else: system_dict["skipped_tests_lists"].append(test); print_status("Skipped"); return system_dict
def test_initializer_xavier_normal(system_dict): forward = True test = "test_initializer_xavier_normal" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append(gtf.convolution(output_channels=16, uid="conv1")) network.append(gtf.batch_normalization(uid="bn1")) network.append(gtf.relu(uid="relu1")) network.append(gtf.convolution(output_channels=16, uid="conv2")) network.append(gtf.batch_normalization(uid="bn2")) network.append(gtf.relu(uid="relu2")) network.append(gtf.max_pooling(uid="pool1")) network.append(gtf.flatten(uid="flatten1")) network.append(gtf.fully_connected(units=1024, uid="fc1")) network.append(gtf.dropout(drop_probability=0.2, uid="dp1")) network.append(gtf.fully_connected(units=2, uid="fc2")) gtf.Compile_Network(network, use_gpu=False, network_initializer="xavier_normal") x = np.random.rand(1, 1, 64, 64) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict
def test_layer_concatenate(system_dict): forward = True test = "test_layer_concatenate" system_dict["total_tests"] += 1 print_start(test, system_dict["total_tests"]) if (forward): try: gtf = prototype(verbose=0) gtf.Prototype("sample-project-1", "sample-experiment-1") network = [] network.append(gtf.convolution(output_channels=16, uid="conv1")) network.append(gtf.batch_normalization(uid="bn1")) network.append(gtf.relu(uid="relu1")) network.append(gtf.max_pooling(uid="pool1")) subnetwork = [] branch1 = [] branch1.append(gtf.convolution(output_channels=16, uid="conv3_1_1")) branch1.append(gtf.batch_normalization(uid="bn3_1_1")) branch1.append(gtf.convolution(output_channels=16, uid="conv3_1_2")) branch1.append(gtf.batch_normalization(uid="bn3_1_2")) branch2 = [] branch2.append(gtf.convolution(output_channels=16, uid="conv3_2_1")) branch2.append(gtf.batch_normalization(uid="bn3_2_1")) branch3 = [] branch3.append(gtf.identity(uid="identity1")) subnetwork.append(branch1) subnetwork.append(branch2) subnetwork.append(branch3) subnetwork.append(gtf.concatenate(uid="concat1")) network.append(subnetwork) network.append(gtf.convolution(output_channels=16, uid="conv4")) network.append(gtf.batch_normalization(uid="bn4")) network.append(gtf.relu(uid="relu3")) network.append(gtf.max_pooling(uid="pool4")) network.append(gtf.flatten(uid="flatten1")) network.append(gtf.fully_connected(units=1024, uid="fc1")) network.append(gtf.dropout(drop_probability=0.2, uid="dp1")) network.append(gtf.fully_connected(units=2, uid="fc2")) gtf.Compile_Network(network, use_gpu=False) x = np.random.rand(1, 1, 64, 64) x = mx.nd.array(x) y = gtf.system_dict["local"]["model"].forward(x) system_dict["successful_tests"] += 1 print_status("Pass") except Exception as e: system_dict["failed_tests_exceptions"].append(e) system_dict["failed_tests_lists"].append(test) forward = False print_status("Fail") else: system_dict["skipped_tests_lists"].append(test) print_status("Skipped") return system_dict