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https://github.com/qurator-spk/eynollah.git
synced 2026-05-13 01:13:54 +02:00
do_prediction*(): avoid unnecessary tiles, simplify…
- calculation for number of tiles: sometimes one less tile is needed by making the previous last tile half-full on the right side - calculation of window margins: fix case if dimension extends to full image shape - simplify (identifiers, slicing etc)
This commit is contained in:
Robert Sachunsky
parent
d8c83d6137
commit
cefe596f8b
1 changed files with 81 additions and 178 deletions
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@ -482,15 +482,18 @@ class Eynollah:
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nxf = math.ceil(img_w / float(width_mid))
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nyf = math.ceil(img_h / float(height_mid))
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_i = []
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batch_j = []
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batch_x_u = []
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batch_x_d = []
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batch_y_u = []
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batch_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3), dtype=np.float16)
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batch = 0
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img_patch = np.zeros((n_batch_inference,
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img_height_model,
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img_width_model,
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3), dtype=np.float16)
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for i in range(nxf):
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for j in range(nyf):
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index_x_d = i * width_mid
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@ -504,18 +507,18 @@ class Eynollah:
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index_y_u = img_h
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index_y_d = img_h - img_height_model
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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batch_i.append(i)
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batch_j.append(j)
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batch_x_u.append(index_x_u)
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batch_x_d.append(index_x_d)
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batch_y_d.append(index_y_d)
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batch_y_u.append(index_y_u)
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img_patch[batch_indexer] = img[index_y_d:index_y_u,
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index_x_d:index_x_u]
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batch_indexer += 1
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img_patch[batch] = img[index_y_d: index_y_u,
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index_x_d: index_x_u]
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batch += 1
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if (batch_indexer == n_batch_inference or
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if (batch == n_batch_inference or
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# last batch
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i == nxf - 1 and j == nyf - 1):
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self.logger.debug("predicting patches on %s", str(img_patch.shape))
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@ -539,75 +542,25 @@ class Eynollah:
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if thresholding_for_artificial_class:
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seg_art = label_p_pred[:, :, :, artificial_class] >= threshold_art_class
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch]
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for batch in range(range):
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where = np.index_exp[batch_y_d[batch]: batch_y_u[batch],
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batch_x_d[batch]: batch_x_u[batch]]
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iny = slice(margin if batch_j[batch] else None,
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(-margin or None) if batch_j[batch] < nyf - 1 else None)
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inx = slice(margin if batch_i[batch] else None,
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(-margin or None) if batch_i[batch] < nxf - 1 else None)
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inbox = np.index_exp[iny, inx]
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prediction[where][inbox] = seg[batch][inbox]
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if thresholding_for_artificial_class:
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seg_in_art = seg_art[indexer_inside_batch]
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mask_artificial_class[where][inbox] = seg_art[batch][inbox]
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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where = np.index_exp[index_y_d_in:index_y_u_in,
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index_x_d_in:index_x_u_in]
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if (i_batch == 0 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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margin:]
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elif (i_batch == 0 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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margin:]
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elif (i_batch == 0 and
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j_batch != 0 and
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j_batch != nyf - 1):
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inbox = np.index_exp[margin:-margin or None,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch != 0 and
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j_batch != nyf - 1):
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inbox = np.index_exp[margin:-margin or None,
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margin:]
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elif (i_batch != 0 and
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i_batch != nxf - 1 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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margin:-margin or None]
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elif (i_batch != 0 and
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i_batch != nxf - 1 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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margin:-margin or None]
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else:
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inbox = np.index_exp[margin:-margin or None,
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margin:-margin or None]
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prediction[where][inbox] = seg_in[inbox]
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if thresholding_for_artificial_class:
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mask_artificial_class[where][inbox] = seg_in_art[inbox]
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indexer_inside_batch += 1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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batch_i = []
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batch_j = []
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batch_x_u = []
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batch_x_d = []
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batch_y_u = []
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batch_y_d = []
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batch = 0
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img_patch[:] = 0
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if thresholding_for_artificial_class:
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@ -685,123 +638,73 @@ class Eynollah:
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confidence = np.zeros((img_h, img_w))
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if thresholding_for_artificial_class:
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mask_artificial_class = np.zeros((img_h, img_w), dtype=bool)
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nxf = math.ceil(img_w / float(width_mid))
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nyf = math.ceil(img_h / float(height_mid))
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nxf = math.ceil((img_w - 2.0 * margin) / width_mid)
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nyf = math.ceil((img_h - 2.0 * margin) / height_mid)
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3), dtype=np.float16)
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batch_i = []
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batch_j = []
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batch_x_u = []
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batch_x_d = []
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batch_y_u = []
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batch_y_d = []
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batch = 0
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img_patch = np.zeros((n_batch_inference,
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img_height_model,
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img_width_model,
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3), dtype=np.float16)
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for i in range(nxf):
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for j in range(nyf):
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - img_width_model
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - img_height_model
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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batch_i.append(i)
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batch_j.append(j)
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batch_x_u.append(index_x_u)
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batch_x_d.append(index_x_d)
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batch_y_d.append(index_y_d)
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batch_y_u.append(index_y_u)
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img_patch[batch_indexer] = img[index_y_d:index_y_u,
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index_x_d:index_x_u]
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batch_indexer += 1
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if (batch_indexer == n_batch_inference or
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img_patch[batch] = img[index_y_d: index_y_u,
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index_x_d: index_x_u]
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batch += 1
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if (batch == n_batch_inference or
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# last batch
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i == nxf - 1 and j == nyf - 1):
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self.logger.debug("predicting patches on %s", str(img_patch.shape))
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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conf = label_p_pred[tuple(np.indices(seg.shape)) + (seg,)]
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if thresholding_for_artificial_class:
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seg_art = label_p_pred[:, :, :, artificial_class] >= threshold_art_class
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch]
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conf_in = conf[indexer_inside_batch]
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for batch in range(batch):
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where = np.index_exp[batch_y_d[batch]: batch_y_u[batch],
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batch_x_d[batch]: batch_x_u[batch]]
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iny = slice(margin if batch_j[batch] else None,
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(-margin or None) if batch_j[batch] < nyf - 1 else None)
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inx = slice(margin if batch_i[batch] else None,
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(-margin or None) if batch_i[batch] < nxf - 1 else None)
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inbox = np.index_exp[iny, inx]
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prediction[where][inbox] = seg[batch][inbox]
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confidence[where][inbox] = conf[batch][inbox]
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if thresholding_for_artificial_class:
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seg_in_art = seg_art[indexer_inside_batch]
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mask_artificial_class[where][inbox] = seg_art[batch][inbox]
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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where = np.index_exp[index_y_d_in:index_y_u_in,
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index_x_d_in:index_x_u_in]
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if (i_batch == 0 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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margin:]
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elif (i_batch == 0 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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margin:]
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elif (i_batch == 0 and
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j_batch != 0 and
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j_batch != nyf - 1):
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inbox = np.index_exp[margin:-margin or None,
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0:-margin or None]
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elif (i_batch == nxf - 1 and
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j_batch != 0 and
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j_batch != nyf - 1):
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inbox = np.index_exp[margin:-margin or None,
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margin:]
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elif (i_batch != 0 and
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i_batch != nxf - 1 and
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j_batch == 0):
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inbox = np.index_exp[0:-margin or None,
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margin:-margin or None]
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elif (i_batch != 0 and
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i_batch != nxf - 1 and
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j_batch == nyf - 1):
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inbox = np.index_exp[margin:,
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margin:-margin or None]
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else:
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inbox = np.index_exp[margin:-margin or None,
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margin:-margin or None]
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prediction[where][inbox] = seg_in[inbox]
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confidence[where][inbox] = conf_in[inbox]
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if thresholding_for_artificial_class:
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mask_artificial_class[where][inbox] = seg_in_art[inbox]
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indexer_inside_batch += 1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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batch_i = []
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batch_j = []
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batch_x_u = []
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batch_x_d = []
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batch_y_u = []
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batch_y_d = []
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batch = 0
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img_patch[:] = 0
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if thresholding_for_artificial_class:
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