code stringlengths 3 6.57k |
|---|
range(self.nsteps) |
self.compute_next_lfstep(momentum, proposal, pwidth) |
compute_next_lfstep(self, momentum, proposal, pwidth) |
np.linalg.solve(proposal.loghess, momentum) |
self.evaluate_logdens(pstate) |
get_corrfact(self, mom, mom_new, currstate, proposal) |
np.linalg.solve(proposal.loghess, mom_new) |
np.log(np.linalg.det(proposal.loghess) |
np.linalg.solve(currstate.loghess, mom) |
np.log(np.linalg.det(currstate.loghess) |
test_random_crop_and_resize_op_c(plot=False) |
logger.info("test_random_crop_and_resize_op_c") |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
c_vision.Decode() |
c_vision.RandomResizedCrop((256, 512) |
data1.map(input_columns=["image"], operations=decode_op) |
data1.map(input_columns=["image"], operations=random_crop_and_resize_op) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
data2.map(input_columns=["image"], operations=decode_op) |
zip(data1.create_dict_iterator(num_epochs=1) |
data2.create_dict_iterator(num_epochs=1) |
RandomResizedCrop() |
cv2.resize(original, (512, 256) |
diff_mse(crop_and_resize, original) |
logger.info("random_crop_and_resize_op_{}, mse: {}".format(num_iter + 1, mse) |
crop_and_resize_images.append(crop_and_resize) |
original_images.append(original) |
visualize_list(original_images, crop_and_resize_images) |
test_random_crop_and_resize_op_py(plot=False) |
logger.info("test_random_crop_and_resize_op_py") |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
py_vision.Decode() |
py_vision.RandomResizedCrop((256, 512) |
py_vision.ToTensor() |
mindspore.dataset.transforms.py_transforms.Compose(transforms1) |
data1.map(input_columns=["image"], operations=transform1) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
py_vision.Decode() |
py_vision.ToTensor() |
mindspore.dataset.transforms.py_transforms.Compose(transforms2) |
data2.map(input_columns=["image"], operations=transform2) |
zip(data1.create_dict_iterator(num_epochs=1) |
data2.create_dict_iterator(num_epochs=1) |
transpose(1, 2, 0) |
astype(np.uint8) |
transpose(1, 2, 0) |
astype(np.uint8) |
cv2.resize(original, (512, 256) |
diff_mse(crop_and_resize, original) |
logger.info("random_crop_and_resize_op_{}, mse: {}".format(num_iter + 1, mse) |
crop_and_resize_images.append(crop_and_resize) |
original_images.append(original) |
visualize_list(original_images, crop_and_resize_images) |
test_random_crop_and_resize_01() |
logger.info("test_random_crop_and_resize_01") |
config_get_set_seed(0) |
config_get_set_num_parallel_workers(1) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
c_vision.Decode() |
c_vision.RandomResizedCrop((256, 512) |
data1.map(input_columns=["image"], operations=decode_op) |
data1.map(input_columns=["image"], operations=random_crop_and_resize_op) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
py_vision.Decode() |
py_vision.RandomResizedCrop((256, 512) |
py_vision.ToTensor() |
mindspore.dataset.transforms.py_transforms.Compose(transforms) |
data2.map(input_columns=["image"], operations=transform) |
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN) |
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN) |
ds.config.set_seed(original_seed) |
ds.config.set_num_parallel_workers(original_num_parallel_workers) |
test_random_crop_and_resize_02() |
logger.info("test_random_crop_and_resize_02") |
config_get_set_seed(0) |
config_get_set_num_parallel_workers(1) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
c_vision.Decode() |
c_vision.RandomResizedCrop((256, 512) |
data1.map(input_columns=["image"], operations=decode_op) |
data1.map(input_columns=["image"], operations=random_crop_and_resize_op) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
py_vision.Decode() |
py_vision.RandomResizedCrop((256, 512) |
py_vision.ToTensor() |
mindspore.dataset.transforms.py_transforms.Compose(transforms) |
data2.map(input_columns=["image"], operations=transform) |
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN) |
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN) |
ds.config.set_seed(original_seed) |
ds.config.set_num_parallel_workers(original_num_parallel_workers) |
test_random_crop_and_resize_03() |
logger.info("test_random_crop_and_resize_03") |
config_get_set_seed(0) |
config_get_set_num_parallel_workers(1) |
ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
c_vision.Decode() |
c_vision.RandomResizedCrop((256, 512) |
data1.map(input_columns=["image"], operations=decode_op) |
data1.map(input_columns=["image"], operations=random_crop_and_resize_op) |
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