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Implemented chunked matching. (Finished chunked best match selection)

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Sam Perry
2016-03-23 21:26:06 +00:00
parent 4d2d4788f7
commit b73ec56a2c
1 changed files with 31 additions and 14 deletions
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src/sppysound/database.py
+31 -14
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@@ -383,7 +383,6 @@ class Matcher:
distance_accum = np.zeros((target_times.shape[0], source_sample_indexes[-1][-1]))
# Allocate memory for storing accumulated distances between
# source and target grains
pdb.set_trace()
x_size = target_times.shape[0]
y_size = int(source_sample_indexes[-1][-1])
@@ -460,13 +459,18 @@ class Matcher:
self.logger.info("Calculating the closest {0} overall matches...". format(self.match_quantity))
i = 0
# Allocate memory for storing chunks.
membuff = np.zeros((chunk_size, chunk_size))
chunk_vals = np.zeros((chunk_size, chunk_size))
chunk_inds = np.tile(np.arange(chunk_size), (chunk_size, 1))
# Allocate memory for storing the best matches for each target
# grain
match_indexes = np.empty((x_size, self.match_quantity)).fill(None)
match_indexes = np.empty((x_size, self.match_quantity))
match_indexes.fill(np.nan)
# Allocate memory for storing the match distance of these grains.
match_vals = np.empty((x_size, self.match_quantity)).fill(np.inf)
match_vals = np.empty((x_size, self.match_quantity))
match_vals.fill(np.inf)
pdb.set_trace()
while i < x_size:
j = chunk_size
if i+j > x_size:
@@ -481,20 +485,33 @@ class Matcher:
print(k)
# Read the current chunk to memory
self.output_db.data["distance_accum"].read_direct(membuff, np.s_[i:i+j, k:k+l], np.s_[0:j, 0:l])
# Find all matches from the current chunk that are closer
# than the worst match from previous chunks.
closer_matches = membuff < match_vals.max(axis=1)
self.output_db.data["distance_accum"].read_direct(chunk_vals, np.s_[i:i+j, k:k+l], np.s_[0:j, 0:l])
# Append the chunk distances to the best matches so far.
vals_append = np.append(match_vals[i:i+j], chunk_vals[i:i+j], axis=1)
c_inds = chunk_inds[0:j] + i
m = np.arange(len(vals_append))[:, np.newaxis]
# Sort all values to intergrate new chunk distances with
# previous best matches.
vals_sort = np.argsort(vals_append, axis=1)
inds_append = np.append(match_indexes[i:i+j], c_inds, axis=1)
best_match_inds = inds_append[m, vals_sort]
match_indexes[i:i+j] = best_match_inds[:, :self.match_quantity]
best_match_vals = vals_append[m, vals_sort]
match_vals[i:i+j] = best_match_vals[:, :self.match_quantity]
k += chunk_size
i += chunk_size
chunk_match_inds = np.argsort(membuff, axis=1)
# Sort indexes so that best matches are at the start of the array.
match_indexes = distance_accum.argsort(axis=1)[:, :self.match_quantity]
match_grain_inds = self.calculate_db_inds(match_indexes, source_sample_indexes)
pdb.set_trace()
# Generate the path to the data group that will store the match
# data in the HDF5 file.
datafile_path = ''.join(("match/", target_entry.name))
@@ -652,7 +669,7 @@ class Synthesizer:
match_index = np.random.randint(matches.shape[0])
match_db_ind, match_grain_ind = matches[match_index]
with self.match_db.analysed_audio[match_db_ind] as match_sample:
match_sample.generate_grain_times(match_grain_size, match_overlap)
match_sample.generate_grain_times(match_grain_size, match_overlap, save_times=True)
# TODO: Make proper fix for grain index offset of 1
match_grain = match_sample[match_grain_ind-1]
@@ -663,7 +680,7 @@ class Synthesizer:
# Calculate garin times for sample to allow for
# indexing.
target_sample.generate_grain_times(match_grain_size, match_overlap)
target_sample.generate_grain_times(match_grain_size, match_overlap, save_times=True)
match_grain = self.enforce_rms(match_grain, match_sample, match_grain_ind, target_sample, target_grain_ind)
@@ -673,7 +690,7 @@ class Synthesizer:
# Calculate grain times for sample to allow for
# indexing.
target_sample.generate_grain_times(match_grain_size, match_overlap)
target_sample.generate_grain_times(match_grain_size, match_overlap, save_times=True)
match_grain = self.enforce_pitch(match_grain, match_sample, match_grain_ind, target_sample, target_grain_ind)