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author | Evan Shelhamer <shelhamer@imaginarynumber.net> | 2014-01-23 21:05:17 -0800 |
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committer | Evan Shelhamer <shelhamer@imaginarynumber.net> | 2014-01-23 22:31:29 -0800 |
commit | 96da93c8a632fb66126df56c1c0ed7d63687ca29 (patch) | |
tree | ecc35b89816bf9e8c21ed2573b6ad25d449aee2f /python | |
parent | f17cfa00f899e512a603e8bcce08770253e434ee (diff) | |
download | caffeonacl-96da93c8a632fb66126df56c1c0ed7d63687ca29.tar.gz caffeonacl-96da93c8a632fb66126df56c1c0ed7d63687ca29.tar.bz2 caffeonacl-96da93c8a632fb66126df56c1c0ed7d63687ca29.zip |
default power_wrapper batch size to 10 (aeca741a69 cont'd)
default command line arg and function arg to 10
remove global BATCH_SIZE in favor of arg
Diffstat (limited to 'python')
-rw-r--r-- | python/caffe/imagenet/power_wrapper.py | 21 |
1 files changed, 9 insertions, 12 deletions
diff --git a/python/caffe/imagenet/power_wrapper.py b/python/caffe/imagenet/power_wrapper.py index 5fa5c393..da5fdfd7 100644 --- a/python/caffe/imagenet/power_wrapper.py +++ b/python/caffe/imagenet/power_wrapper.py @@ -31,9 +31,6 @@ IMAGE_CENTER = int((IMAGE_DIM - CROPPED_DIM) / 2) CROP_MODES = ['center_only', 'corners', 'selective_search'] -# NOTE: this must match the setting in the prototxt that is used! -BATCH_SIZE = 10 - # Load the imagenet mean file IMAGENET_MEAN = np.load( os.path.join(os.path.dirname(__file__), 'ilsvrc_2012_mean.npy')) @@ -187,7 +184,7 @@ def _assemble_images_selective_search(image_fnames): return images_df -def assemble_batches(image_fnames, crop_mode='center_only', batch_size=256): +def assemble_batches(image_fnames, crop_mode='center_only', batch_size=10): """ Assemble DataFrame of image crops for feature computation. @@ -201,7 +198,7 @@ def assemble_batches(image_fnames, crop_mode='center_only', batch_size=256): image, and take each enclosing subwindow. Output: - df_batches: list of DataFrames, each one of BATCH_SIZE rows. + df_batches: list of DataFrames, each one of batch_size rows. Each row has 'image', 'filename', and 'window' info. Column 'image' contains (X x 3 x 227 x 227) ndarrays. Column 'filename' contains source filenames. @@ -219,23 +216,23 @@ def assemble_batches(image_fnames, crop_mode='center_only', batch_size=256): else: raise Exception("Unknown mode: not in {}".format(CROP_MODES)) - # Make sure the DataFrame has a multiple of BATCH_SIZE rows: + # Make sure the DataFrame has a multiple of batch_size rows: # just fill the extra rows with NaN filenames and all-zero images. N = images_df.shape[0] - remainder = N % BATCH_SIZE + remainder = N % batch_size if remainder > 0: zero_image = np.zeros_like(images_df['image'].iloc[0]) remainder_df = pd.DataFrame([{ 'filename': None, 'image': zero_image, 'window': [0, 0, 0, 0] - }] * (BATCH_SIZE - remainder)) + }] * (batch_size - remainder)) images_df = images_df.append(remainder_df) N = images_df.shape[0] - # Split into batches of BATCH_SIZE. - ind = np.arange(N) / BATCH_SIZE - df_batches = [images_df[ind == i] for i in range(N / BATCH_SIZE)] + # Split into batches of batch_size. + ind = np.arange(N) / batch_size + df_batches = [images_df[ind == i] for i in range(N / batch_size)] return df_batches @@ -273,7 +270,7 @@ if __name__ == "__main__": gflags.DEFINE_string( "images_file", "", "File that contains image filenames.") gflags.DEFINE_string( - "batch_size", 256, "Number of image crops to let through in one go") + "batch_size", 10, "Number of image crops to let through in one go") gflags.DEFINE_string( "output", "", "The output DataFrame HDF5 filename.") gflags.DEFINE_string( |