def _read_model(self, model_dir, checkpoints):
""" Reads in the final model parameters from model dir. If self.analyse_checkpoints = True, the
checkpoint models are read in as well.
Parameters
----------
model_dir (str): Path to the model.
checkpoints (list): List of the checkpoint strings.
Returns
-------
models (list): list of the loaded GQCNN models
"""
# Determine model name
model_root, model_name = os.path.split(model_dir)
self.logger.info("Analyzing model %s" % model_name)
# Load model.
self.logger.info("Loading model %s" % model_dir)
models = []
# Load all checkpoint models
if self.analyse_checkpoints:
for checkpoint in checkpoints:
if checkpoint == 'final':
model = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose)
else:
model = get_gqcnn_model(verbose=self.verbose).load(
model_dir,
verbose=self.verbose,
checkpoint_step=checkpoint)
model.open_session()
models.append(model)
else:
model = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose)
model.open_session()
models.append(model)
return models
def _run_trial(self, analysis_config, train_config, dataset_dir, split_name, output_dir, model_name, train_progress_dict, trial_progress_dict, hyperparam_summary, gpu_avail="", cpu_cores_avail=[], backend='tf'):
trial_progress_dict['status'] = TrialStatus.RUNNING
try:
os.system("taskset -pc {} {}".format(",".join(str(i) for i in cpu_cores_avail), os.getpid()))
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_avail
gqcnn = get_gqcnn_model(backend, verbose=False)(train_config['gqcnn'], verbose=False)
trainer = get_gqcnn_trainer(backend)(gqcnn,
dataset_dir,
split_name,
output_dir,
train_config,
name=model_name,
progress_dict=train_progress_dict,
verbose=False)
self._run(trainer)
with open(os.path.join(output_dir, model_name, 'hyperparam_summary.json'), 'wb') as fhandle:
json.dump(hyperparam_summary, fhandle, indent=GeneralConstants.JSON_INDENT)
train_progress_dict['training_status'] = 'analyzing'
analyzer = GQCNNAnalyzer(analysis_config, verbose=False)
_, _, init_train_error, final_train_error, init_train_loss, final_train_loss, init_val_error, final_val_error, norm_final_val_error = analyzer.analyze(os.path.join(output_dir, model_name), output_dir)
analysis_dict = {}
analysis_dict['init_train_error'] = init_train_error
analysis_dict['final_train_error'] = final_train_error
analysis_dict['init_train_loss'] = init_train_loss
analysis_dict['final_train_loss'] = final_train_loss
analysis_dict['init_val_error'] = init_val_error
analysis_dict['final_val_error'] = final_val_error
analysis_dict['norm_final_val_error'] = norm_final_val_error
train_progress_dict['analysis'] = analysis_dict
train_progress_dict['training_status'] = 'finished'
trial_progress_dict['status'] = TrialStatus.FINISHED
sys.exit(0)
except Exception as e:
trial_progress_dict['status'] = TrialStatus.EXCEPTION
trial_progress_dict['error_msg'] = str(e)
sys.exit(0)
def _run_prediction(self, model_dir, model_output_dir, data_dir,
noise_analysis, depth_analysis, perturb_analysis,
single_analysis):
"""Predict the outcome of the file for a single model."""
# Read in model config.
model_config_filename = os.path.join(model_dir, "config.json")
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# Load model.
self.logger.info("Loading model %s" % (model_dir))
log_file = None
for handler in self.logger.handlers:
if isinstance(handler, logging.FileHandler):
log_file = handler.baseFilename
gqcnn = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose, log_file=log_file)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
angular_bins = gqcnn.angular_bins
# Load data
if noise_analysis:
image_arr, pose_arr, labels, width_arr, file_arr, noise_arr = self._read_data(
data_dir, noise=True)
elif depth_analysis:
image_arr, pose_arr, labels, width_arr, file_arr, depth_arr = self._read_data(
data_dir, depth=True)
elif perturb_analysis:
image_arr, pose_arr, labels, width_arr, file_arr, perturb_arr = self._read_data(
data_dir, perturb=True)
elif single_analysis:
image_arr, pose_arr, labels, width_arr, file_arr, perturb_arr = self._read_data(
data_dir, perturb=True)
else:
image_arr, pose_arr, labels, width_arr, file_arr, obj_arr = self._read_data(
data_dir)
# Predict outcomes
predictions = gqcnn.predict(image_arr, pose_arr)
gqcnn.close_session()
results = BinaryClassificationResult(predictions[:, 1], labels)
# Log the results
if noise_analysis:
# Analyse the error rates in regard to the noise levels of the images
noise_levels = np.unique(noise_arr)
levels = len(noise_levels)
for current_noise in noise_levels:
pred = predictions[noise_arr[:, 0] == current_noise]
lab = labels[noise_arr[:, 0] == current_noise]
res = BinaryClassificationResult(pred[:, 1], lab)
self._plot_histograms(pred[:, 1], lab, str(current_noise),
model_output_dir)
self.logger.info("Noise: %.4f Model %s error rate: %.3f" %
(current_noise, model_dir, res.error_rate))
self.logger.info(
"Noise: %.4f Model %s loss: %.3f" %
(current_noise, model_dir, res.cross_entropy_loss))
elif depth_analysis:
# Analyse the error rates in regard to the grasping depth in the images
depth_levels = np.unique(depth_arr)
levels = len(depth_levels)
for current_depth in depth_levels:
if current_depth == -1:
depth_mode = 'original'
else:
depth_mode = 'relative %.2f' % (current_depth)
pred = predictions[depth_arr == current_depth]
lab = labels[depth_arr == current_depth]
res = BinaryClassificationResult(pred[:, 1], lab)
self._plot_histograms(pred[:, 1], lab, depth_mode,
model_output_dir)
self.logger.info("Depth %s Model %s error rate: %.3f" %
(depth_mode, model_dir, res.error_rate))
self.logger.info(
"Depth: %s Model %s loss: %.3f" %
(depth_mode, model_dir, res.cross_entropy_loss))
elif perturb_analysis:
# Analyse the error rates in regard to the grasping perturb in the images
perturb_levels = np.unique(perturb_arr)
print("Perturb levels: ", perturb_levels)
_rot = len(np.unique(perturb_arr[:, 0]))
_trans = len(np.unique(perturb_arr[:, 1]))
try:
_transy = len(np.unique(perturb_arr[:, 2]))
except:
_transy = 0
print("No translation in y included")
if _rot >= 2 and _trans <= 1 and _transy <= 1:
perturbation = 'rotation'
perturb_unit = 'deg'
index = 0
elif _rot <= 1 and _trans >= 2 and _transy <= 1:
perturbation = 'translation'
perturb_unit = 'pixel'
index = 1
elif _rot <= 1 and _trans <= 1 and _transy >= 2:
perturbation = 'translationy'
perturb_unit = 'pixel'
index = 2
else:
raise ValueError(
"Perturbation array includes at least two different perturbation types. Can't be handled. Abort."
)
return None
levels = len(perturb_levels)
accuracies = []
for current_perturb in perturb_levels:
pred = predictions[perturb_arr[:, index] == current_perturb]
lab = labels[perturb_arr[:, index] == current_perturb]
res = BinaryClassificationResult(pred[:, 1], lab)
perturb_mode = perturbation + ' %.0f ' % (
current_perturb) + perturb_unit
self._plot_histograms(
pred[:, 1], lab,
perturbation + '_%.0f_' % (current_perturb) + perturb_unit,
model_output_dir)
self.logger.info("Grasp %s Model %s error rate: %.3f" %
(perturb_mode, model_dir, res.error_rate))
accuracies.append(100 - res.error_rate)
self.logger.info(
"Grasp %s Model %s loss: %.3f" %
(perturb_mode, model_dir, res.cross_entropy_loss))
self._plot_grasp_perturbations(perturb_levels, accuracies,
model_output_dir, perturbation)
elif single_analysis:
# Analyse the error rates in regard to the grasping perturb in the images
perturb_levels = np.unique(perturb_arr)
_rot = np.count_nonzero(perturb_arr[:, 0])
_trans = np.count_nonzero(perturb_arr[:, 1])
_transy = np.count_nonzero(perturb_arr[:, 2])
_scalez = np.count_nonzero(perturb_arr[:, 3])
_scalex = np.count_nonzero(perturb_arr[:, 4])
if _rot >= 1 and _trans == 0 and _transy == 0 and _scalez == 0 and _scalex == 0:
index = 0
perturbation = 'rotation'
elif _rot == 0 and _trans >= 1 and _transy == 0 and _scalez == 0 and _scalex == 0:
perturbation = 'translation'
index = 1
elif _rot == 0 and _trans == 0 and _transy >= 1 and _scalez == 0 and _scalex == 0:
perturbation = 'translationy'
index = 2
elif _rot == 0 and _trans == 0 and _transy == 0 and _scalez >= 1 and _scalex == 0:
perturbation = 'scale_height'
index = 3
elif _rot == 0 and _trans == 0 and _transy == 0 and _scalez == 0 and _scalex >= 1:
perturbation = 'scalex'
index = 4
else:
perturbation = 'mixed'
index = 5
# Create new output dir for single file and perturbation mode
print(len(perturb_arr))
if len(perturb_arr) == 1:
print("New output direction is: ", model_output_dir)
else:
model_output_dir = os.path.join(
model_output_dir,
str(file_arr[0][0]) + '_' + str(file_arr[0][1]) + '_' +
perturbation)
print("New output direction is: ", model_output_dir)
if not os.path.exists(model_output_dir):
os.mkdir(model_output_dir)
# Set up new logger.
self.logger = Logger.get_logger(self.__class__.__name__,
log_file=os.path.join(
model_output_dir,
"analysis.log"),
silence=(not self.verbose),
global_log_file=self.verbose)
levels = len(perturb_arr)
abs_pred_errors = []
if levels == 1:
self.logger.info(
"Mixed perturbation. Translationx %.1f, Translationy %.1f, "
"Rotation %.1f, Scale_height %.1f, Scale x %.1f" %
(perturb_arr[0][1], perturb_arr[0][2], perturb_arr[0][0],
perturb_arr[0][3], perturb_arr[0][4]))
pred = predictions
lab = labels
res = BinaryClassificationResult(pred[:, 1], lab)
self.logger.info("Grasp %s Model %s prediction: %.3f" %
(perturbation, model_dir, pred[:, 1]))
self.logger.info("Grasp %s Model %s error rate: %.3f" %
(perturbation, model_dir, res.error_rate))
self.logger.info(
"Grasp %s Model %s loss: %.3f" %
(perturbation, model_dir, res.cross_entropy_loss))
else:
for current_perturb in perturb_levels:
pred = predictions[perturb_arr[:,
index] == current_perturb]
lab = labels[perturb_arr[:, index] == current_perturb]
res = BinaryClassificationResult(pred[:, 1], lab)
if perturbation == 'rotation':
perturb_mode = 'rotation %.0f deg' % (current_perturb)
elif perturbation == 'translation':
perturb_mode = 'translation in x %.0f pixel' % (
current_perturb)
elif perturbation == 'translationy':
perturb_mode = 'translation in y %.0f pixel' % (
current_perturb)
elif perturbation == 'scale_height':
perturb_mode = 'scaling depth by %.0f' % (
current_perturb)
elif perturbation == 'scalex':
perturb_mode = 'scaling x by %.0f' % (current_perturb)
pos_errors, neg_errors = self._calculate_prediction_errors(
pred[:, 1], lab)
# Only append positive errors if grasp was positive.
if pos_errors:
abs_pred_errors.append(pos_errors)
self.logger.info("Grasp %s Model %s prediction: %.3f" %
(perturb_mode, model_dir, pred[:, 1]))
self.logger.info("Grasp %s Model %s error rate: %.3f" %
(perturb_mode, model_dir, res.error_rate))
self.logger.info(
"Grasp %s Model %s loss: %.3f" %
(perturb_mode, model_dir, res.cross_entropy_loss))
if pos_errors:
self._plot_single_grasp_perturbations(
perturb_levels, abs_pred_errors, model_output_dir,
perturbation)
else:
levels = 1
self._plot_histograms(predictions[:, 1], labels, '',
model_output_dir)
self.logger.info("Model %s error rate: %.3f" %
(model_dir, results.error_rate))
self.logger.info("Model %s loss: %.3f" %
(model_dir, results.cross_entropy_loss))
if obj_arr is not None and 'Cornell' in data_dir:
unique = np.unique(obj_arr).tolist()
object_label = pd.read_csv(
DATA_PATH + "Cornell/original/z.txt",
sep=" ",
header=None,
usecols=[1, 2]).drop_duplicates().to_numpy()
true_pos = dict()
false_neg = dict()
false_pos = dict()
true_neg = dict()
for obj in unique:
obj = int(obj)
true_pos[object_label[obj, 1]] = 0
false_pos[object_label[obj, 1]] = 0
true_neg[object_label[obj, 1]] = 0
false_neg[object_label[obj, 1]] = 0
for obj, pred, label in zip(obj_arr, predictions[:, 1],
labels):
if label == 1 and pred >= 0.5:
true_pos[object_label[obj, 1]] += 1
elif label == 1 and pred < 0.5:
false_neg[object_label[obj, 1]] += 1
elif label == 0 and pred >= 0.5:
false_pos[object_label[obj, 1]] += 1
elif label == 0 and pred < 0.5:
true_neg[object_label[obj, 1]] += 1
print(true_pos)
self._export_object_analysis(true_pos, false_neg, false_pos,
true_neg, model_output_dir)
# Log the ratios
pos_lab = len(labels[labels == 1])
neg_lab = len(labels[labels == 0])
true_pos = len(results.true_positive_indices)
true_neg = len(results.true_negative_indices)
false_pos = neg_lab - true_neg
false_neg = pos_lab - true_pos
self.logger.info("%d samples, %d grasps" %
(len(labels), len(labels) / levels))
self.logger.info("%d positive grasps, %d negative grasps" %
(pos_lab / levels, neg_lab / levels))
self.logger.info("Model overall accuracy %.2f %%" %
(100 * results.accuracy))
self.logger.info("Accuracy on positive grasps: %.2f %%" %
(true_pos / pos_lab * 100))
self.logger.info("Accuracy on negative grasps: %.2f %%" %
(true_neg / neg_lab * 100))
self.logger.info("True positive samples: %d" % true_pos)
self.logger.info("True negative samples: %d" % true_neg)
self.logger.info("Correct predictions: %d" % (true_pos + true_neg))
self.logger.info("False positive samples: %d" % false_pos)
self.logger.info("False negative samples: %d" % false_neg)
self.logger.info("False predictions: %d" % (false_pos + false_pos))
cnt = 0 # Counter for grouping the same images with different noise/depth levels
if self.num_images is None or self.num_images > len(width_arr):
self.num_images = len(width_arr)
steps = int(len(width_arr) / self.num_images)
for j in range(0, len(width_arr), steps):
try:
if file_arr[j][1] != file_arr[j - 1][1]:
cnt = 0
else:
cnt += 1
except:
cnt += 1
if noise_analysis:
image = self._plot_grasp(image_arr[j],
width_arr[j],
results,
j,
noise_arr=noise_arr)
elif depth_analysis:
image = self._plot_grasp(image_arr[j],
width_arr[j],
results,
j,
depth_arr=depth_arr)
elif perturb_analysis or single_analysis:
print("Plot grasp")
image = self._plot_grasp(image_arr[j],
width_arr[j],
results,
j,
perturb_arr=perturb_arr)
else:
image = self._plot_grasp(image_arr[j],
width_arr[j],
results,
j,
plt_results=False)
try:
if noise_analysis or depth_analysis or perturb_analysis or single_analysis:
image.save(
os.path.join(
model_output_dir, "%05d_%03d_example_%03d.png" %
(file_arr[j][0], file_arr[j][1], cnt)))
else:
image.save(
os.path.join(
model_output_dir, "%05d_%03d.png" %
(file_arr[j][0], file_arr[j][1])))
# data = self.scale(image_arr[j][:, :, 0])
# image = Image.fromarray(data).convert('RGB').resize((300, 300), resample=Image.NEAREST)
# image.save(os.path.join(model_output_dir, "%05d_%03d_orig.png" % (file_arr[j][0], file_arr[j][1])))
except:
image.save(
os.path.join(model_output_dir, "Example_%03d.png" % (cnt)))
if single_analysis:
print("Plotting depth image")
j = int(len(image_arr) / 2)
# Plot pure depth image without prediction labeling.
image = self._plot_grasp(image_arr[j],
width_arr[j],
results,
j,
plt_results=False)
image.save(os.path.join(model_output_dir, "Depth_image.png"))
return results
def visualise(self, model_dir, output_dir, data_dir):
# Determine model name
model_name = ""
model_root = model_dir
while model_name == "" and model_root != "":
model_root, model_name = os.path.split(model_root)
output_dir = os.path.join(output_dir, "Visualisation/")
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# Read in model config.
model_config_filename = os.path.join(model_dir, "config.json")
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# Set up logger
self.logger = Logger.get_logger(self.__class__.__name__,
log_file=os.path.join(
output_dir, "analysis.log"),
silence=(not self.verbose),
global_log_file=self.verbose)
self.logger.info("Analyzing model %s" % (model_name))
self.logger.info("Saving output to %s" % (output_dir))
mixture = False
if "Cornell" in model_dir:
model_name = "Cornell"
elif "DexNet" in model_dir:
model_name = "DexNet"
if "Cornell" in data_dir:
data_name = "Cornell"
elif "DexNet" in data_dir:
data_name = "DexNet"
elif "Both" in data_dir:
data_name = "mixed"
mixture = True
# Load model.
self.logger.info("Loading model %s" % (model_dir))
log_file = None
for handler in self.logger.handlers:
if isinstance(handler, logging.FileHandler):
log_file = handler.baseFilename
gqcnn = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose, log_file=log_file)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
angular_bins = gqcnn.angular_bins
if mixture:
image_arr, pose_arr, labels, width_arr, file_arr, obj_arr, identity_arr = self._read_data(
data_dir, mixture=True)
else:
image_arr, pose_arr, labels, width_arr, file_arr, obj_arr = self._read_data(
data_dir)
print("Object arr: ", obj_arr)
# Predict outcomes
predictions = gqcnn.predict(image_arr, pose_arr)
if predictions.shape[1] == 1:
print("Only 1 image given. No t-SNE analysis of network possible")
else:
# Setting colors and labels
color = []
monotone = False
if mixture:
for label, identity in zip(labels, identity_arr):
if identity == 0:
# Cornell
if label == 0:
# negative
color.append('#FF8000')
else:
# positive
color.append('#2D702F')
# DexNet
if label == 0:
# negative
color.append('#FF0404')
else:
# positive
color.append('#23C328')
if len(np.unique(labels)) == 1:
monotone = True
if labels[0] == 0:
data_name += " negatives"
pop_a = mpatches.Patch(color='#FF8000',
label='Negative Cornell')
pop_b = mpatches.Patch(color='#FF0404',
label='Negative DexNet')
else:
data_name += " positives"
pop_a = mpatches.Patch(color='#2D702F',
label='Positive Cornell')
pop_b = mpatches.Patch(color='#23C328',
label='Positive DexNet')
else:
pop_a = mpatches.Patch(color='#FF8000',
label='Negative Cornell')
pop_b = mpatches.Patch(color='#FF0404',
label='Negative DexNet')
pop_c = mpatches.Patch(color='#2D702F',
label='Positive Cornell')
pop_d = mpatches.Patch(color='#23C328',
label='Positive DexNet')
else:
color = ['r' if truth == 0 else 'g' for truth in labels]
pop_a = mpatches.Patch(color='r', label='Negative grasp')
pop_b = mpatches.Patch(color='g', label='Positive grasp')
# t-SNE
tsne_out = sklearn.manifold.TSNE(
n_components=2).fit_transform(predictions)
plt.scatter(tsne_out[:, 0], tsne_out[:, 1], marker='o', c=color)
if mixture and not monotone:
plt.legend(handles=[pop_a, pop_b, pop_c, pop_d])
else:
plt.legend(handles=[pop_a, pop_b])
plt.title("TSNE output of %s data on a GQCNN trained on %s" %
(data_name, model_name))
plt.savefig(output_dir + "/" + model_name + "_model_" + data_name +
"_data_TSNE.png")
plt.close()
# PCA
pca_out = sklearn.decomposition.PCA(
n_components=2).fit_transform(predictions)
plt.scatter(pca_out[:, 0], pca_out[:, 1], marker='o', c=color)
plt.title("PCA output of %s data on a GQCNN trained on %s" %
(data_name, model_name))
if mixture and not monotone:
plt.legend(handles=[pop_a, pop_b, pop_c, pop_d])
else:
plt.legend(handles=[pop_a, pop_b])
plt.savefig(output_dir + "/" + model_name + "_model_" + data_name +
"_data_PCA.png")
plt.close()
full_var_names[15])).numpy()
self.b_p1 = tf.Variable(reader.get_tensor(
full_var_names[16])).numpy()
self.w_4 = np.hstack((self.w_41.T, self.w_42.T)).T
@staticmethod
def _leaky_relu(x, alpha=.1):
return tf.maximum(alpha * x, x)
# Replicated GQCNN model in tensorflow.keras style from checkpoint
# We need to do this as the loaded GQCNN does not have a format where you can use outputs of specific layers and
# calculate the gradient
if tf.__version__ != '2.4.1':
gqcnn = get_gqcnn_model(verbose=True).load(GQCNN_MODEL, verbose=True)
gqcnn.open_session()
if '4.0' in GQCNN_MODEL:
model = GQCNN4().build()
else:
model = GQCNN2().build()
model.compile(run_eagerly=True,
loss=tf.keras.losses.SparseCategoricalCrossentropy())
n_examples = np.load(DATA_DIR + depth_str)['arr_0'].shape[0]
# GQCNN outputs two predictions, the first for the probability of the grasp being bad and
# the second for the probability of the grasp being good. They sum to one, so
# the second one of them is always chosen. Compare to gqcnn/analysis/analyzer.py line 292
for y_c in [0, 1]:
