Create a box by mentioning the top-left (x, y) and bottom-right (X, Y) coordinates
-Say x, y, X, Y are 10, 20, 40, 50 respectively
bb = BB([10, 20, 40, 50])You get the following attributes for free
-
-bb.x=10 (top left - x)
-bb.y=20 (top left - y)
-bb.X=40 (bottom right - x)
-bb.Y=50 (bottom right - y)
-bb.w=30 (width)
-bb.h=30 (height)
-bb.xc=25.0 (center x)
-bb.yc=35.0 (center y)
-bb.c=(25.0, 35.0) (center)
-bb.area=900 (area)
-bb.shape=(30, 30) (height, width)
-from torch_snippets import show, read, P, pdassets = P("/Users/yeshwanth.y/code/torch_snippets/assets/")
-im = read(assets / "Preamble.png")
-df = pd.read_csv(assets / "Preamble.csv")
-show(df.head())
-df = to_absolute(df, *im.shape[:2])
-show(df.head())
-df = to_relative(df, *im.shape[:2])
-show(df.head())| - | x | -y | -X | -Y | -text | -block_id | -
|---|---|---|---|---|---|---|
| 0 | -135 | -181 | -308 | -218 | -ConstITUtIO | -0 | -
| 1 | -156 | -264 | -217 | -284 | -NLTHE | -1 | -
| 2 | -218 | -264 | -276 | -284 | -PEOPLE | -1 | -
| 3 | -267 | -265 | -295 | -282 | -OF | -1 | -
| 4 | -297 | -264 | -341 | -284 | -INDIA, | -1 | -
| - | x | -y | -X | -Y | -text | -block_id | -
|---|---|---|---|---|---|---|
| 0 | -135 | -181 | -308 | -218 | -ConstITUtIO | -0 | -
| 1 | -156 | -264 | -217 | -284 | -NLTHE | -1 | -
| 2 | -218 | -264 | -276 | -284 | -PEOPLE | -1 | -
| 3 | -267 | -265 | -295 | -282 | -OF | -1 | -
| 4 | -297 | -264 | -341 | -284 | -INDIA, | -1 | -
| - | x | -y | -X | -Y | -text | -block_id | -
|---|---|---|---|---|---|---|
| 0 | -0.249538 | -0.253501 | -0.569316 | -0.305322 | -ConstITUtIO | -0 | -
| 1 | -0.288355 | -0.369748 | -0.401109 | -0.397759 | -NLTHE | -1 | -
| 2 | -0.402957 | -0.369748 | -0.510166 | -0.397759 | -PEOPLE | -1 | -
| 3 | -0.493530 | -0.371148 | -0.545287 | -0.394958 | -OF | -1 | -
| 4 | -0.548983 | -0.369748 | -0.630314 | -0.397759 | -INDIA, | -1 | -
show(im, df=df, sz=10)
show(im, df=to_absolute(df, *im.shape[:2]), sz=10)
_df = to_absolute(df, *im.shape[:2])
-show(im, df=to_relative(_df, *im.shape[:2]), sz=10)
_df = combine_xyXY_to_bb(_df)
-show(im, df=to_relative(_df, *im.shape[:2]), sz=10)
to_relative(_df, *im.shape[:2])| - | text | -block_id | -bb | -
|---|---|---|---|
| 0 | -ConstITUtIO | -0 | -[0.24953789279112754, 0.2535014005602241, 0.56... | -
| 1 | -NLTHE | -1 | -[0.28835489833641403, 0.3697478991596639, 0.40... | -
| 2 | -PEOPLE | -1 | -[0.4029574861367837, 0.3697478991596639, 0.510... | -
| 3 | -OF | -1 | -[0.49353049907578556, 0.3711484593837535, 0.54... | -
| 4 | -INDIA, | -1 | -[0.5489833641404805, 0.3697478991596639, 0.630... | -
| ... | -... | -... | -... | -
| 68 | -GIVE | -13 | -[0.4011090573012939, 0.7366946778711485, 0.478... | -
| 69 | -TO | -13 | -[0.4879852125693161, 0.7366946778711485, 0.536... | -
| 70 | -oUrSELVES | -13 | -[0.5508317929759704, 0.7394957983193278, 0.706... | -
| 71 | -THIS | -13 | -[0.7190388170055453, 0.7366946778711485, 0.783... | -
| 72 | -CONSTITUTION. | -14 | -[0.23290203327171904, 0.7689075630252101, 0.44... | -
73 rows ร 3 columns
----isin (bboxes1, bboxes2, return_matrix=True)
return indexes of those boxes from bboxes1 that are completely inside bboxes2
---merge_by_bb (df1, df2, suffixes=('_x', '_y'), iou_threshold=0.1)
Merge df1 columns to df2 by using iou Make sure both df1 & df2 are relative or both absolute
- - -Letโs load the iris dataset first
-from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
-
-data = load_iris()
-X, y = data.data, data.target
-X_trn, X_val, y_trn, y_val = train_test_split(X, y, random_state=42)โฆ and create the data loaders
-from torch_snippets.markup2 import AD
-from torch.utils.data import TensorDataset
-
-trn_ds = TensorDataset(*[torch.Tensor(i) for i in [X_trn, y_trn]])
-trn_dl = DataLoader(trn_ds, batch_size=32)
-
-val_ds = TensorDataset(*[torch.Tensor(i) for i in [X_val, y_val]])
-val_dl = DataLoader(val_ds, batch_size=32)
-
-AD(next(iter(val_dl)))
-```โฏ AttrDict โฏ
-next(iter(val_dl))[]
- 0 - ๐ฆtensor[32, 4] n=128 xโ[0.100, 7.900] ฮผ=3.488 ฯ=2.034 - ID:#56f55949
- 1 - ๐ฆtensor[32] xโ[0., 2.000] ฮผ=0.969 ฯ=0.861 - ID:#b365fa0a
-
-```Next weโll import Capsule and a few decorators that will tell the model to change itโs mode to train/test during the fit function
from torch_snippets.trainer.capsule import Capsule, train, validate, predictCreate the neural network and define itโs forward function as usual pytorch business. Only difference now is that youโll also add self.loss_fn and self.optimizer attributes in the init
class IrisModel(Capsule):
- def __init__(self):
- super().__init__()
- self.model = nn.Sequential(
- nn.BatchNorm1d(4),
- nn.Linear(4, 16),
- nn.Dropout(0.2),
- nn.BatchNorm1d(16),
- nn.ReLU(inplace=True),
- nn.Linear(16, 8),
- nn.Dropout(0.2),
- nn.BatchNorm1d(8),
- nn.ReLU(inplace=True),
- nn.Linear(8, 3),
- )
- self.loss_fn = nn.CrossEntropyLoss()
- self.optimizer = optim.Adam(self.parameters())
-
- def forward(self, x):
- return self.model(x)To fully describe the modelโs behaviour we still need to define three functions
-1. train_batch
-2. validate_batch and,
-3. predict which is optional
like so
-
- @train
- def train_batch(self, batch):
- x, y = batch
- _y = self.forward(x)
- loss = self.loss_fn(_y, y.long())
- return {"loss": loss}
-
- @validate
- def validate_batch(self, batch=None, dl=None):
- if dl is not None:
- output = []
- for batch in dl:
- output.extend(self.predict(batch=batch)["val_acc"])
- return np.mean(output)
- x, y = batch
- _y = self.forward(x)
- loss = self.loss_fn(_y, y.long())
- acc = (y == _y.max(-1)[1]).float().mean()
- return {"val_loss": loss, "val_acc": acc}
-
- @predict
- def predict(self, batch=None, dl=None):
- if dl is not None:
- output = []
- for batch in dl:
- output.extend(self.predict(batch=batch))
- return output
- x, y = batch
- _y = self.forward(x)
- o = _y.max(-1)[1].cpu().detach().numpy().tolist()
- return otrain_batch and validate_batch functions. You can return as many metrics during training and validation, they will be auto logged.train_batch is the key loss, as this is used to compute gradients.*We could now create the modelโฆ
-model = IrisModel()
-model.device = "cpu"โฆ and run model.fit with an optional number of logs to print to the console
model.fit(trn_dl, val_dl, num_epochs=100, print_total=2, device="cpu")EPOCH: 1.000 val_acc: 0.281 loss: 1.118 val_loss: 1.159 (0.05s - 5.02s remaining)
-EPOCH: 50.000 val_acc: 1.000 loss: 0.490 val_loss: 0.406 (0.18s - 0.18s remaining)
-EPOCH: 100.000 val_acc: 1.000 loss: 0.238 val_loss: 0.162 (0.31s - 0.00s remaining)
model.evaluate accepts a validation data loader that will repeatedly call validate_batch and return aggregated metrics
model.evaluate(val_dl, device="cpu")EPOCH: 1.000 val_acc: 1.000 val_loss: 0.164 (0.00s - 0.00s remaining){'epoch_val_acc': np.float64(1.0),
- 'epoch_val_loss': np.float64(0.16414348781108856)}from torch_snippets.loader import *
-from sklearn.datasets import make_moons
-
-np.random.seed(10)
-x, y = make_moons(1000, noise=0.1)
-df = pd.DataFrame({"x1": x[:, 0], "x2": x[:, 1], "y": y})
-
-Chart(df).mark_circle().encode(x="x1:Q", y="x2:Q", color="y:N").interactive()Refer to altair-viz.github.io for more awesome charts.
-torch-snippets exposes a confusion matrix function CM as an example
n = 10
-a = "qwertyuiopasdfghjklzxcvbnm"
-truth = np.random.randint(4, size=1000000)
-pred = np.random.randint(4, size=1000000)
-show(CM(truth=truth, pred=pred, mapping={i: a for i, a in enumerate(a)}))
-# mapping is optional precision recall f1-score support
-
- 0 0.25 0.25 0.25 250150
- 1 0.25 0.25 0.25 250245
- 2 0.25 0.25 0.25 249836
- 3 0.25 0.25 0.25 249769
-
- accuracy 0.25 1000000
- macro avg 0.25 0.25 0.25 1000000
-weighted avg 0.25 0.25 0.25 1000000
-df = pd.DataFrame(
- {
- "truth": [randint(n) for _ in range(1000)],
- "pred": [randint(n) for _ in range(1000)],
- }
-)
-show(CM(df, "truth", "pred", mapping={i: a for i, a in enumerate(a)}))
-# mapping is optional precision recall f1-score support
-
- 0 0.13 0.14 0.13 92
- 1 0.08 0.09 0.08 101
- 2 0.13 0.12 0.13 107
- 3 0.06 0.06 0.06 105
- 4 0.12 0.11 0.11 94
- 5 0.12 0.09 0.10 115
- 6 0.08 0.10 0.09 88
- 7 0.08 0.07 0.08 113
- 8 0.09 0.09 0.09 99
- 9 0.12 0.15 0.13 86
-
- accuracy 0.10 1000
- macro avg 0.10 0.10 0.10 1000
-weighted avg 0.10 0.10 0.10 1000
-df = pd.DataFrame(
- {
- "truth": [choose("abcd") for _ in range(1000)],
- "pred": [choose("abcd") for _ in range(1000)],
- }
-)
-show(CM(df, "truth", "pred"))
-# mapping is optional precision recall f1-score support
-
- a 0.25 0.29 0.27 229
- b 0.28 0.29 0.28 256
- c 0.27 0.24 0.26 267
- d 0.26 0.25 0.25 248
-
- accuracy 0.27 1000
- macro avg 0.26 0.27 0.26 1000
-weighted avg 0.27 0.27 0.26 1000
----spider (df, id_column=None, title=None, max_values=None, padding=1.25, - global_scale=False, ax=None, sz=10)
*Plot a spider chart based on the given dataframe.
-Parameters: - df: pandas DataFrame The input dataframe containing the data to be plotted. - id_column: str, optional The column name to be used as the identifier for each data point. If not provided, the index of the dataframe will be used. - title: str, optional The title of the spider chart. - max_values: dict, optional A dictionary specifying the maximum values for each category. If not provided, the maximum values will be calculated based on the data. - padding: float, optional The padding factor to be applied when calculating the maximum values. Default is 1.25. - global_scale: bool or float, optional If False, each category will have its own maximum value. If True, a single maximum value will be used for all categories. If a float value is provided, it will be used as the maximum value for all categories. - ax: matplotlib Axes, optional The axes on which to plot the spider chart. If not provided, a new figure and axes will be created. - sz: float, optional The size of the figure (both width and height) in inches. Default is 10.
-Returns: - None
-Example usage: spider(df, id_column=โmodelโ, title=โSpider Chartโ, max_values={โcategory1โ: 10, โcategory2โ: 20}, padding=1.5)*
-import pandas as pd
-
-spider(
- pd.DataFrame(
- {
- "x": [*"abcde"],
- "c1": [10, 11, 12, 13, 14],
- "c2": [0.1, 0.3, 0.4, 0.1, 0.9],
- "c3": [1e5, 2e5, 3.5e5, 8e4, 5e4],
- "c4": [9, 12, 5, 2, 0.2],
- "test": [1, 1, 1, 1, 5],
- },
- index=[*"abcde"],
- ),
- title="Sample Spider",
- padding=1.1,
-)
---UpSetAltair (data=None, title='', subtitle='', sets=None, abbre=None, - sort_by='frequency', sort_order='ascending', width=1200, - height=700, height_ratio=0.6, - horizontal_bar_chart_width=300, color_range=['#55A8DB', - '#3070B5', '#30363F', '#F1AD60', '#DF6234', '#BDC6CA'], - highlight_color='#EA4667', glyph_size=200, - set_label_bg_size=1000, line_connection_size=2, - horizontal_bar_size=20, vertical_bar_label_size=16, - vertical_bar_padding=20)
*This function generates Altair-based interactive UpSet plots.
-Parameters: - data (pandas.DataFrame): Tabular data containing the membership of each element (row) in exclusive intersecting sets (column). - sets (list): List of set names of interest to show in the UpSet plots. This list reflects the order of sets to be shown in the plots as well. - abbre (list): Abbreviated set names. - sort_by (str): โfrequencyโ or โdegreeโ - sort_order (str): โascendingโ or โdescendingโ - width (int): Vertical size of the UpSet plot. - height (int): Horizontal size of the UpSet plot. - height_ratio (float): Ratio of height between upper and under views, ranges from 0 to 1. - horizontal_bar_chart_width (int): Width of horizontal bar chart on the bottom-right. - color_range (list): Color to encode sets. - highlight_color (str): Color to encode intersecting sets upon mouse hover. - glyph_size (int): Size of UpSet glyph (โฌค). - set_label_bg_size (int): Size of label background in the horizontal bar chart. - line_connection_size (int): width of lines in matrix view. - horizontal_bar_size (int): Height of bars in the horizontal bar chart. - vertical_bar_label_size (int): Font size of texts in the vertical bar chart on the top. - vertical_bar_padding (int): Gap between a pair of bars in the vertical bar charts.*
----upsetaltair_top_level_configuration (base, legend_orient='top-left', - legend_symbol_size=30)
*Configure the top-level settings for an UpSet plot in Altair.
-Parameters: - base: The base chart to configure. - legend_orient: The orientation of the legend. Default is โtop-leftโ. - legend_symbol_size: The size of the legend symbols. Default is 30.
-Returns: - The configured chart.*
-df| - | truth | -pred | -
|---|---|---|
| 0 | -c | -d | -
| 1 | -c | -c | -
| 2 | -d | -d | -
| 3 | -c | -a | -
| 4 | -d | -c | -
| ... | -... | -... | -
| 995 | -c | -c | -
| 996 | -a | -c | -
| 997 | -b | -a | -
| 998 | -b | -c | -
| 999 | -a | -d | -
1000 rows ร 2 columns
-# import numpy as np
-
-# i = np.random.randn(300, 7) > 0.33
-# df = pd.DataFrame(i.astype(int))
-# df.columns = [rand() for _ in range(len(df.columns))]
-# show(df)
-
-# UpSetAltair(
-# df,
-# sets=list(df.columns),
-# abbre=list(df.columns),
-# sort_by="frequencey",
-# sort_order="ascending",
-# )| - | 4wST3l | -v6Vv72 | -WKSGX4 | -LBlidv | -g0LDKa | -xpK2f5 | -pW4oKO | -
|---|---|---|---|---|---|---|---|
| 0 | -0 | -1 | -0 | -1 | -0 | -0 | -1 | -
| 1 | -1 | -0 | -0 | -1 | -0 | -1 | -0 | -
| 2 | -1 | -0 | -1 | -0 | -0 | -1 | -0 | -
| 3 | -1 | -0 | -0 | -0 | -0 | -0 | -1 | -
| 4 | -0 | -0 | -1 | -0 | -1 | -0 | -1 | -
| 5 | -0 | -0 | -0 | -1 | -0 | -0 | -0 | -
| 6 | -1 | -0 | -0 | -1 | -1 | -0 | -1 | -
| 7 | -1 | -1 | -1 | -1 | -0 | -1 | -1 | -
| 8 | -1 | -0 | -0 | -1 | -0 | -1 | -1 | -
| 9 | -1 | -0 | -0 | -1 | -0 | -0 | -0 | -
| 10 | -1 | -0 | -0 | -0 | -0 | -0 | -1 | -
| 11 | -1 | -0 | -1 | -1 | -0 | -0 | -0 | -
| 12 | -0 | -1 | -0 | -1 | -0 | -0 | -1 | -
| 13 | -1 | -0 | -0 | -1 | -0 | -1 | -1 | -
| 14 | -1 | -1 | -0 | -1 | -0 | -0 | -0 | -
| ... | -... | -... | -... | -... | -... | -... | -... | -
| 285 | -1 | -0 | -0 | -0 | -0 | -0 | -0 | -
| 286 | -1 | -0 | -0 | -1 | -1 | -1 | -1 | -
| 287 | -0 | -0 | -1 | -1 | -0 | -1 | -0 | -
| 288 | -0 | -1 | -1 | -1 | -0 | -0 | -0 | -
| 289 | -0 | -1 | -1 | -1 | -0 | -0 | -0 | -
| 290 | -0 | -1 | -1 | -1 | -1 | -0 | -0 | -
| 291 | -0 | -0 | -1 | -0 | -1 | -0 | -0 | -
| 292 | -0 | -0 | -1 | -1 | -0 | -0 | -1 | -
| 293 | -1 | -0 | -0 | -0 | -0 | -1 | -0 | -
| 294 | -0 | -0 | -0 | -0 | -0 | -0 | -1 | -
| 295 | -0 | -1 | -1 | -1 | -0 | -0 | -0 | -
| 296 | -0 | -0 | -0 | -1 | -0 | -1 | -0 | -
| 297 | -1 | -0 | -1 | -0 | -0 | -0 | -1 | -
| 298 | -0 | -1 | -1 | -0 | -1 | -0 | -0 | -
| 299 | -0 | -0 | -1 | -0 | -0 | -0 | -0 | -
/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_multi' is deprecated. Use 'selection_point'
- warnings.warn(message, AltairDeprecationWarning, stacklevel=1)
-/opt/miniconda3/lib/python3.12/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_single' is deprecated. Use 'selection_point'
- warnings.warn(message, AltairDeprecationWarning, stacklevel=1)--------------------------------------------------------------------------- -SchemaValidationError Traceback (most recent call last) -Cell In[14], line 8 - 5 df.columns = [rand() for _ in range(len(df.columns))] - 6 show(df) -----> 8 UpSetAltair( - 9 df, - 10 sets=list(df.columns), - 11 abbre=list(df.columns), - 12 sort_by="frequencey", - 13 sort_order="ascending", - 14 ) - -Cell In[10], line 239, in UpSetAltair(data, title, subtitle, sets, abbre, sort_by, sort_order, width, height, height_ratio, horizontal_bar_chart_width, color_range, highlight_color, glyph_size, set_label_bg_size, line_connection_size, horizontal_bar_size, vertical_bar_label_size, vertical_bar_padding) - 177 base = ( - 178 alt.Chart(data) - 179 .transform_filter(legend_selection) - (...) - 232 ) - 233 ) - 234 # Now, we have data in the following format: - 235 # count, set, is_intersect, degree, intersection_id, set_abbre - 236 - 237 # Cardinality by intersecting sets (vertical bar chart) - 238 vertical_bar = ( ---> 239 base.mark_bar(color=main_color, size=vertical_bar_size) - 240 .encode( - 241 x=alt.X( - 242 "intersection_id:N", - 243 axis=alt.Axis(grid=False, labels=False, ticks=False, domain=True), - 244 sort=x_sort, - 245 title=None, - 246 ), - 247 y=alt.Y( - 248 "max(count):Q", - 249 axis=alt.Axis(grid=False, tickCount=3, orient="right"), - 250 title="Intersection Size", - 251 ), - 252 color=brush_color, - 253 tooltip=tooltip, - 254 ) - 255 .properties(width=matrix_width, height=vertical_bar_chart_height) - 256 ) - 258 vertical_bar_text = vertical_bar.mark_text( - 259 color=main_color, dy=-10, size=vertical_bar_label_size - 260 ).encode(text=alt.Text("count:Q", format=".0f")) - 262 vertical_bar_chart = (vertical_bar + vertical_bar_text).add_selection( - 263 color_selection - 264 ) - -File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/mixins.py:2786, in MarkMethodMixin.mark_bar(self, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds) - 2784 copy = self.copy(deep=False) # type: ignore[attr-defined] - 2785 if any(val is not Undefined for val in kwds.values()): --> 2786 copy.mark = core.MarkDef(type="bar", **kwds) - 2787 else: - 2788 copy.mark = "bar" - -File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:23720, in MarkDef.__init__(self, type, align, angle, aria, ariaRole, ariaRoleDescription, aspect, bandSize, baseline, binSpacing, blend, clip, color, continuousBandSize, cornerRadius, cornerRadiusBottomLeft, cornerRadiusBottomRight, cornerRadiusEnd, cornerRadiusTopLeft, cornerRadiusTopRight, cursor, description, dir, discreteBandSize, dx, dy, ellipsis, fill, fillOpacity, filled, font, fontSize, fontStyle, fontWeight, height, href, innerRadius, interpolate, invalid, limit, line, lineBreak, lineHeight, minBandSize, opacity, order, orient, outerRadius, padAngle, point, radius, radius2, radius2Offset, radiusOffset, shape, size, smooth, stroke, strokeCap, strokeDash, strokeDashOffset, strokeJoin, strokeMiterLimit, strokeOffset, strokeOpacity, strokeWidth, style, tension, text, theta, theta2, theta2Offset, thetaOffset, thickness, timeUnitBandPosition, timeUnitBandSize, tooltip, url, width, x, x2, x2Offset, xOffset, y, y2, y2Offset, yOffset, **kwds) - 22901 def __init__( - 22902 self, - 22903 type: Union[ - (...) - 23718 **kwds, - 23719 ): -> 23720 super(MarkDef, self).__init__( - 23721 type=type, - 23722 align=align, - 23723 angle=angle, - 23724 aria=aria, - 23725 ariaRole=ariaRole, - 23726 ariaRoleDescription=ariaRoleDescription, - 23727 aspect=aspect, - 23728 bandSize=bandSize, - 23729 baseline=baseline, - 23730 binSpacing=binSpacing, - 23731 blend=blend, - 23732 clip=clip, - 23733 color=color, - 23734 continuousBandSize=continuousBandSize, - 23735 cornerRadius=cornerRadius, - 23736 cornerRadiusBottomLeft=cornerRadiusBottomLeft, - 23737 cornerRadiusBottomRight=cornerRadiusBottomRight, - 23738 cornerRadiusEnd=cornerRadiusEnd, - 23739 cornerRadiusTopLeft=cornerRadiusTopLeft, - 23740 cornerRadiusTopRight=cornerRadiusTopRight, - 23741 cursor=cursor, - 23742 description=description, - 23743 dir=dir, - 23744 discreteBandSize=discreteBandSize, - 23745 dx=dx, - 23746 dy=dy, - 23747 ellipsis=ellipsis, - 23748 fill=fill, - 23749 fillOpacity=fillOpacity, - 23750 filled=filled, - 23751 font=font, - 23752 fontSize=fontSize, - 23753 fontStyle=fontStyle, - 23754 fontWeight=fontWeight, - 23755 height=height, - 23756 href=href, - 23757 innerRadius=innerRadius, - 23758 interpolate=interpolate, - 23759 invalid=invalid, - 23760 limit=limit, - 23761 line=line, - 23762 lineBreak=lineBreak, - 23763 lineHeight=lineHeight, - 23764 minBandSize=minBandSize, - 23765 opacity=opacity, - 23766 order=order, - 23767 orient=orient, - 23768 outerRadius=outerRadius, - 23769 padAngle=padAngle, - 23770 point=point, - 23771 radius=radius, - 23772 radius2=radius2, - 23773 radius2Offset=radius2Offset, - 23774 radiusOffset=radiusOffset, - 23775 shape=shape, - 23776 size=size, - 23777 smooth=smooth, - 23778 stroke=stroke, - 23779 strokeCap=strokeCap, - 23780 strokeDash=strokeDash, - 23781 strokeDashOffset=strokeDashOffset, - 23782 strokeJoin=strokeJoin, - 23783 strokeMiterLimit=strokeMiterLimit, - 23784 strokeOffset=strokeOffset, - 23785 strokeOpacity=strokeOpacity, - 23786 strokeWidth=strokeWidth, - 23787 style=style, - 23788 tension=tension, - 23789 text=text, - 23790 theta=theta, - 23791 theta2=theta2, - 23792 theta2Offset=theta2Offset, - 23793 thetaOffset=thetaOffset, - 23794 thickness=thickness, - 23795 timeUnitBandPosition=timeUnitBandPosition, - 23796 timeUnitBandSize=timeUnitBandSize, - 23797 tooltip=tooltip, - 23798 url=url, - 23799 width=width, - 23800 x=x, - 23801 x2=x2, - 23802 x2Offset=x2Offset, - 23803 xOffset=xOffset, - 23804 y=y, - 23805 y2=y2, - 23806 y2Offset=y2Offset, - 23807 yOffset=yOffset, - 23808 **kwds, - 23809 ) - -File /opt/miniconda3/lib/python3.12/site-packages/altair/vegalite/v5/schema/core.py:149, in AnyMark.__init__(self, *args, **kwds) - 148 def __init__(self, *args, **kwds): ---> 149 super(AnyMark, self).__init__(*args, **kwds) - -File /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:771, in SchemaBase.__init__(self, *args, **kwds) - 768 object.__setattr__(self, "_kwds", kwds) - 770 if DEBUG_MODE and self._class_is_valid_at_instantiation: ---> 771 self.to_dict(validate=True) - -File /opt/miniconda3/lib/python3.12/site-packages/altair/utils/schemapi.py:978, in SchemaBase.to_dict(self, validate, ignore, context) - 971 self.validate(result) - 972 except jsonschema.ValidationError as err: - 973 # We do not raise `from err` as else the resulting - 974 # traceback is very long as it contains part - 975 # of the Vega-Lite schema. It would also first - 976 # show the less helpful ValidationError instead of - 977 # the more user friendly SchemaValidationError ---> 978 raise SchemaValidationError(self, err) from None - 979 return result - -SchemaValidationError: '-7.628865979381443' is an invalid value for `size`. - --7.628865979381443 is less than the minimum of 0-
ERROR:root:No traceback has been produced, nothing to debug.wrappers for common imgaug functions, that accept image and ocr data frame of bounding boxes
- -from torch_snippets.loader import read, pd, show
-
-IM = read("/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.png")
-DF = pd.read_csv("/Users/yeshwanth.y/code/torch_snippets/assets/Preamble.csv")
-DF = to_relative(DF, *IM.shape[:2])
-DF.head()| - | x | -y | -X | -Y | -text | -block_id | -
|---|---|---|---|---|---|---|
| 0 | -0.249538 | -0.253501 | -0.569316 | -0.305322 | -ConstITUtIO | -0 | -
| 1 | -0.288355 | -0.369748 | -0.401109 | -0.397759 | -NLTHE | -1 | -
| 2 | -0.402957 | -0.369748 | -0.510166 | -0.397759 | -PEOPLE | -1 | -
| 3 | -0.493530 | -0.371148 | -0.545287 | -0.394958 | -OF | -1 | -
| 4 | -0.548983 | -0.369748 | -0.630314 | -0.397759 | -INDIA, | -1 | -
im = IM.copy()
-im = rescale(im, sz=(400, 600))
-show(im, sz=5)
df = DF.copy()
-im = IM.copy()
-im, df = rescale(im, df, (400, 600))
-show(im, df=df, sz=5)
-df.head()--------------------------------------------------------------------------- -ValueError Traceback (most recent call last) -/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in <cell line: 3>() - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=0'>1</a> df = DF.copy() - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=1'>2</a> im = IM.copy() -----> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=2'>3</a> im, df = rescale(im, df, (400, 600)) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=3'>4</a> show(im, df=df, sz=5) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=4'>5</a> df.head() - -/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in rescale(im, bbs, sz) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=110'>111</a> H, W = get_size(sz, h, w) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=111'>112</a> aug = iaa.Resize({"height": H, "width": W}) ---> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=112'>113</a> im, bbs = do(im, bbs, aug) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=113'>114</a> if to_pil: - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=114'>115</a> im = PIL.Image.fromarray(im) - -/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb Cell 8 in do(img, bbs, aug, cval) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=41'>42</a> __df = combine_xyXY_to_bb(__df) - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=42'>43</a> bbs = __df ----> <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=43'>44</a> if bbs == []: - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=44'>45</a> return img, [] - <a href='vscode-notebook-cell:/Users/yeshwanth.y/code/torch_snippets/nbs/imgaug_loader.ipynb#W6sZmlsZQ%3D%3D?line=45'>46</a> return img, bbs - -File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:705, in _comp_method_FRAME.<locals>.f(self, other) - 701 @Appender(f"Wrapper for comparison method {op_name}") - 702 def f(self, other): - 703 axis = 1 # only relevant for Series other case ---> 705 self, other = _align_method_FRAME(self, other, axis, level=None, flex=False) - 707 # See GH#4537 for discussion of scalar op behavior - 708 new_data = dispatch_to_series(self, other, op, axis=axis) - -File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:503, in _align_method_FRAME(left, right, axis, flex, level) - 496 raise ValueError( - 497 "Unable to coerce to Series/DataFrame, " - 498 f"dimension must be <= 2: {right.shape}" - 499 ) - 501 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)): - 502 # GH17901 ---> 503 right = to_series(right) - 505 if flex is not None and isinstance(right, ABCDataFrame): - 506 if not left._indexed_same(right): - -File ~/miniconda3/lib/python3.9/site-packages/pandas/core/ops/__init__.py:465, in _align_method_FRAME.<locals>.to_series(right) - 463 else: - 464 if len(left.columns) != len(right): ---> 465 raise ValueError( - 466 msg.format(req_len=len(left.columns), given_len=len(right)) - 467 ) - 468 right = left._constructor_sliced(right, index=left.columns) - 469 return right - -ValueError: Unable to coerce to Series, length must be 6: given 0-
for i in range(11):
- angle = (i - 5) * 5
- print(angle)
- im = rotate(IM, angle=angle)
- show(im, sz=1)-25
-20
-15
-10
-5
0
5
10
15
20
25
df = DF.copy()
-im = IM.copy()
-im, df = rotate(im, df, 90)
-show(im, df=df, sz=20)
-print(type(im))
-df.head()
<class 'numpy.ndarray'>| - | text | -block_id | -x | -y | -X | -Y | -
|---|---|---|---|---|---|---|
| 0 | -ConstITUtIO | -0 | -0.693277 | -0.249538 | -0.745098 | -0.569316 | -
| 1 | -NLTHE | -1 | -0.600840 | -0.288355 | -0.628852 | -0.401109 | -
| 2 | -PEOPLE | -1 | -0.600840 | -0.402957 | -0.628852 | -0.510166 | -
| 3 | -OF | -1 | -0.603641 | -0.493530 | -0.627451 | -0.545287 | -
| 4 | -INDIA, | -1 | -0.600840 | -0.548983 | -0.628852 | -0.630314 | -
df = DF.copy()
-im = IM.copy()
-im, df = pad(im, df, deltas=(90, 90), cval=0)
-show(im, df=df, sz=20)
-print(type(im))
-df.head()
<class 'numpy.ndarray'>| - | text | -block_id | -x | -y | -X | -Y | -
|---|---|---|---|---|---|---|
| 0 | -ConstITUtIO | -0 | -0.310536 | -0.302521 | -0.550832 | -0.343137 | -
| 1 | -NLTHE | -1 | -0.340111 | -0.394958 | -0.425139 | -0.417367 | -
| 2 | -PEOPLE | -1 | -0.426987 | -0.394958 | -0.506470 | -0.417367 | -
| 3 | -OF | -1 | -0.493530 | -0.396359 | -0.532348 | -0.415966 | -
| 4 | -INDIA, | -1 | -0.536044 | -0.394958 | -0.597043 | -0.417367 | -
All functions will work with data frames that contain either of absolute/relative coordinates, and will preserve the image type (np.ndarray or PIL.Image.Image) too
df = DF.copy()
-im = IM.copy()
-height, width = im.shape
-show(df.head())
-im = PIL.Image.fromarray(im)
-im, df = rotate(im, df, 45, cval=127)
-im, df = pad(im, df, deltas=(200, 200), cval=0)
-im, df = rescale(im, df, sz=(300, 300))
-show(im, df=df, sz=20)
-print(type(im))
-df.head()| - | x | -y | -X | -Y | -text | -block_id | -
|---|---|---|---|---|---|---|
| 0 | -0.249538 | -0.253501 | -0.569316 | -0.305322 | -ConstITUtIO | -0 | -
| 1 | -0.288355 | -0.369748 | -0.401109 | -0.397759 | -NLTHE | -1 | -
| 2 | -0.402957 | -0.369748 | -0.510166 | -0.397759 | -PEOPLE | -1 | -
| 3 | -0.493530 | -0.371148 | -0.545287 | -0.394958 | -OF | -1 | -
| 4 | -0.548983 | -0.369748 | -0.630314 | -0.397759 | -INDIA, | -1 | -
<class 'PIL.Image.Image'>| - | text | -block_id | -x | -y | -X | -Y | -
|---|---|---|---|---|---|---|
| 0 | -ConstITUtIO | -0 | -0.500000 | -0.326667 | -0.613333 | -0.440000 | -
| 1 | -NLTHE | -1 | -0.473333 | -0.383333 | -0.516667 | -0.426667 | -
| 2 | -PEOPLE | -1 | -0.506667 | -0.416667 | -0.550000 | -0.460000 | -
| 3 | -OF | -1 | -0.536667 | -0.446667 | -0.560000 | -0.470000 | -
| 4 | -INDIA, | -1 | -0.550000 | -0.460000 | -0.586667 | -0.493333 | -
Logging is packed in these 5 functions
-reset_logger("trace", console_width=80)
-Trace("Testing {1,2,3}")
-Debug("TESTING {1,2,3}")
-Info("TESTING {1,2,3}")
-Warn("TESTING {1,2,3}")
-Excep("TESTING {1,2,3}")[08/24/24 12:16:05] TRACE Testing {1,2,3} 1838145784.py:<module>:2 --
DEBUG TESTING {1,2,3} 1838145784.py:<module>:3 --
INFO TESTING {1,2,3} 1838145784.py:<module>:4 --
WARNING TESTING {1,2,3} 1838145784.py:<module>:5 --
ERROR TESTING {1,2,3} 1838145784.py:<module>:6 - NoneType: None - - --
Excep will also print a traceback
def do():
- try:
- 1 / 0
- except Exception as e:
- Excep(e)
-
-def do2():
- do()
-
-do2()[08/24/24 12:16:07] ERROR division by zero 1923207781.py:do:5 - ZeroDivisionError: division by - zero - - โญโ Traceback (most recent callโโฎ - โ in do:3 โ - โ โ - โ 1 def do(): โ - โ 2 โ try: โ - โ โฑ 3 โ โ 1 / 0 โ - โ 4 โ except Exception as โ - โ 5 โ โ Excep(e) โ - โ 6 โ - โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ - ZeroDivisionError: division by - zero --
All logging functions have an optional depth that will be helpful to raise the context level to outer functions
-def do():
- Info('In do')
-
-do()[08/24/24 12:16:15] INFO In do 809811040.py:do:2 --
def do2():
- Info('Log will still say it is from `do2` now, to the right of log print')
-
-def do():
- do2()
-
-do()[08/24/24 12:17:13] INFO Log will still say it is from 2099333876.py:do2:2 - `do2` now, to the right of log - print --
def do2():
- Info('But now, log will still say it is from do', depth=1)
-
-def do():
- do2()
-
-do()[08/24/24 12:17:25] INFO But now, log will still say it 4054019042.py:do:5 - is from do --
---in_logger_mode (level:str)
returnโs T/F, checking if logger is in a specific mode or not
----logger_mode (level)
temporarily, using with context, set the level to something else
---get_logger_level ()
get the current loggerโs level
-Letโs log every level in the do function below. We can control what we need to log from outside the functionโs context by
-using with <level>_model():
def do():
- Trace(0)
- Debug(1)
- Info(2)
- Warn(3)
- Excep(4)
-
-
-def line(x):
- sep = "=" * 20
- print(f"{sep}{x}{sep}")
- print(f"{in_excep_mode()=}")
- print(f"{in_warn_mode()=}")
- print(f"{in_info_mode()=}")
- print(f"{in_debug_mode()=}")
- print(f"{in_trace_mode()=}")
-
-
-reset_logger()
-
-with excep_mode():
- line("Excep mode")
- do()
-
-with warn_mode():
- line("Warn mode")
- do()
-
-with info_mode():
- line("Info mode")
- do()
-
-with debug_mode():
- line("Debug mode")
- do()
-
-with trace_mode():
- line("Trace mode")
- do()====================Excep mode====================
-in_excep_mode()=True
-in_warn_mode()=False
-in_info_mode()=False
-in_debug_mode()=False
-in_trace_mode()=False[08/24/24 12:13:08] ERROR 4 3432048509.py:do:6 - NoneType: None - - --
====================Warn mode====================
-in_excep_mode()=False
-in_warn_mode()=True
-in_info_mode()=False
-in_debug_mode()=False
-in_trace_mode()=False[08/24/24 12:13:08] WARNING 3 3432048509.py:do:5 --
ERROR 4 3432048509.py:do:6 - NoneType: None - - --
====================Info mode====================
-in_excep_mode()=False
-in_warn_mode()=False
-in_info_mode()=True
-in_debug_mode()=False
-in_trace_mode()=False[08/24/24 12:13:08] INFO 2 3432048509.py:do:4 --
WARNING 3 3432048509.py:do:5 --
ERROR 4 3432048509.py:do:6 - NoneType: None - - --
====================Debug mode====================
-in_excep_mode()=False
-in_warn_mode()=False
-in_info_mode()=False
-in_debug_mode()=True
-in_trace_mode()=False[08/24/24 12:13:08] DEBUG 1 3432048509.py:do:3 --
INFO 2 3432048509.py:do:4 --
WARNING 3 3432048509.py:do:5 --
ERROR 4 3432048509.py:do:6 - NoneType: None - - --
====================Trace mode====================
-in_excep_mode()=False
-in_warn_mode()=False
-in_info_mode()=False
-in_debug_mode()=False
-in_trace_mode()=True[08/24/24 12:13:08] TRACE 0 3432048509.py:do:2 --
DEBUG 1 3432048509.py:do:3 --
INFO 2 3432048509.py:do:4 --
WARNING 3 3432048509.py:do:5 --
ERROR 4 3432048509.py:do:6 - NoneType: None - - --
The in_<level>_mode gives an additional layer of control, to be used for debugging dynamically. Letโs say, you want to show an image (for the sake of debugging)
def do(im_path):
- from torch_snippets import show, read
- im = read(im_path)
- show(im, sz=3)
- print(im.mean())
-
-do('assets/Preamble.png')
145.5542982442515But now you are happy with your code and donโt want the show, say the code is going to production. A common way out is to just comment that line
-def do(im_path):
- from torch_snippets import show, read
- im = read(im_path)
- # show(im, sz=3) # line is commented, but will need to be re-uncommented any time it needs debugging
- print(im.mean())
-
-do('assets/Preamble.png')145.5542982442515But if you want to re-check, itโs a pain to again uncomment. Not to mention this method is not scalable to 100s of lines of code. The simple way to deal with such transient code that needs to activate only when you want it to, is to enclose in an if in_<level>_mode conditional like so
def do(im_path):
- from torch_snippets import show, read
- im = read(im_path)
- if in_debug_mode():
- show(im, sz=3)
- print(im.mean())
-
-do('assets/Preamble.png')145.5542982442515This way, you can always activate the show by calling do with a temporary with_debug_mode context
with debug_mode():
- do('assets/Preamble.png')
145.5542982442515reset_logger(console_width=100)
-
-with trace_mode():
- with notify_waiting("Downloading"):
- time.sleep(10)
-
-with notify_waiting("Downloading", spinner="guess_the_movie"):
- time.sleep(10)
-
-with notify_waiting("One more message", spinner="earth"):
- time.sleep(3)[08/24/24 12:36:51] TRACE Available Spinners: dict_keys(['dots', 'dots2', 'dots3', 'dots4', 'dots5', 'dots6', 'dots7', 'dots8', 'dots9', 3340474699.py:notify_waiting:41 - 'dots10', 'dots11', 'dots12', 'dots8Bit', 'line', 'line2', 'pipe', 'simpleDots', 'simpleDotsScrolling', 'star', - 'star2', 'flip', 'hamburger', 'growVertical', 'growHorizontal', 'balloon', 'balloon2', 'noise', 'bounce', - 'boxBounce', 'boxBounce2', 'triangle', 'arc', 'circle', 'squareCorners', 'circleQuarters', 'circleHalves', - 'squish', 'toggle', 'toggle2', 'toggle3', 'toggle4', 'toggle5', 'toggle6', 'toggle7', 'toggle8', 'toggle9', - 'toggle10', 'toggle11', 'toggle12', 'toggle13', 'arrow', 'arrow2', 'arrow3', 'bouncingBar', 'bouncingBall', - 'smiley', 'monkey', 'hearts', 'clock', 'earth', 'material', 'moon', 'runner', 'pong', 'shark', 'dqpb', - 'weather', 'christmas', 'grenade', 'point', 'layer', 'betaWave', 'aesthetic', 'guess', 'guess_the_movie']) - - Using: clock --
[08/24/24 12:37:01] INFO Downloading - Completed in 10.01 s contextlib.py:__exit__:144 --
[08/24/24 12:37:11] INFO Downloading - Completed in 10.01 s contextlib.py:__exit__:144 --
[08/24/24 12:37:14] INFO One more message - Completed in 3.00 s contextlib.py:__exit__:144 --
from torch_snippets.torch_loader import Report
-import numpy as np
-import timen_epochs = 3
-report = Report(n_epochs)
-random_walker1 = 0
-random_walker2 = 0
-
-for epoch in range(n_epochs):
- for ix in range(1000):
- report.record(
- pos=epoch + (ix + 1) / 1000,
- loss=random_walker1,
- val_loss=random_walker2,
- end="\r",
- )
- random_walker1 += np.random.normal()
- random_walker2 += np.random.normal()
- time.sleep(0.001)
- report.report_avgs(epoch + 1)
-
-report.plot()EPOCH: 1.000 loss: -6.503 val_loss: -3.093 (1.19s - 2.38s remaining)))
-EPOCH: 2.000 loss: 48.754 val_loss: -6.265 (2.37s - 1.18s remaining))
-EPOCH: 3.000 loss: 38.115 val_loss: -29.732 (3.54s - 0.00s remaining)
n_epochs = 5
-report = Report(n_epochs, old_report=report)
-
-for epoch in range(n_epochs):
- for ix in range(1000):
- report.record(
- pos=epoch + (ix + 1) / 1000,
- loss=random_walker1,
- val_loss=random_walker2,
- end="\r",
- )
- random_walker1 += np.random.normal()
- random_walker2 += np.random.normal()
- time.sleep(0.001)
- report.report_avgs(epoch + 1)EPOCH: 1.000 loss: 29.338 val_loss: -74.955 (1.17s - 4.70s remaining))
-EPOCH: 2.000 loss: 0.340 val_loss: -110.763 (2.35s - 3.52s remaining)))
-EPOCH: 3.000 loss: 30.617 val_loss: -84.599 (3.51s - 2.34s remaining))
-EPOCH: 4.000 loss: 34.309 val_loss: -27.520 (4.68s - 1.17s remaining)
-EPOCH: 5.000 loss: 15.252 val_loss: -46.033 (5.85s - 0.00s remaining)import matplotlib.pyplot as plt
-
-fig, ax = plt.subplots()
-ax.vlines(0, -100, 100, colors=["red"])
-report.plot(ax=ax)
Show is intended to show numpy-arrays/PIL-images
-from torch_snippets import *
-
-im = np.random.rand(100, 100)
-show(im)
show(im, sz=4)
Show will even accept pytorch Tensors and show them as images, even if they are on GPU and have channels first
-It can accept bounding boxes as tuples of (x,y,X,Y) which can be integers (i.e., absolute coordinates) or fractions (between \([0,1]\)). Thereโs provision to give bb_colors and texts as well
show(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)])
-
-show(im, bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)], bb_colors=["r", "g"])
-
-show(
- im,
- bbs=[(0, 0, 0.5, 0.35), (0, 0.2, 0.35, 0.95)],
- bb_colors=["b", "g"],
- texts=["bb1", "bb2"],
- sz=10,
- text_sz=15,
-)Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
It can also read a file path directly and display the image
-show("../docs/images/company_logo_big.png", sz=3)
if the input is not an image or string, show will simply display the given input as intended by jupyter notebook
df = pd.DataFrame(np.random.rand(100, 2))
-show(df)| - | 0 | -1 | -
|---|---|---|
| 0 | -0.242478 | -0.929517 | -
| 1 | -0.971890 | -0.506750 | -
| 2 | -0.139826 | -0.753227 | -
| 3 | -0.864799 | -0.524166 | -
| 4 | -0.563610 | -0.135709 | -
| ... | -... | -... | -
| 95 | -0.379874 | -0.639771 | -
| 96 | -0.993731 | -0.811343 | -
| 97 | -0.621683 | -0.763054 | -
| 98 | -0.844509 | -0.915156 | -
| 99 | -0.314272 | -0.392418 | -
100 rows ร 2 columns
-chart = Chart(df).mark_circle().encode(x="0:Q", y="1:Q")
-show(chart.interactive())display multiple images Subplots is a wapper around plt.subplots that accepts a list of images, number of columns as nc and additional kwargs
-from torch_snippets import subplots
-
-ims = [np.random.rand(100, 100) for _ in range(16)]
-
-subplots(ims, nc=4, sz=5)
subplots(
- ims,
- nc=2,
- sz=(5, 15),
- suptitle="RANDOM IMAGES",
- titles=[f"random_{i}" for i in range(16)],
-)
${missingFields[0]} field.`,
- message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`,
- };
- } else if (missingFields.length > 1) {
- const missingFieldList = missingFields
- .map((field) => {
- return `${field}`;
- })
- .join(", ");
-
- throw {
- name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
- message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`,
- };
- }
- }
-}
-
-let lastQuery = null;
-function showCopyLink(query, options) {
- const language = options.language;
- lastQuery = query;
- // Insert share icon
- const inputSuffixEl = window.document.body.querySelector(
- ".aa-Form .aa-InputWrapperSuffix"
- );
-
- if (inputSuffixEl) {
- let copyButtonEl = window.document.body.querySelector(
- ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
- );
-
- if (copyButtonEl === null) {
- copyButtonEl = window.document.createElement("button");
- copyButtonEl.setAttribute("class", "aa-CopyButton");
- copyButtonEl.setAttribute("type", "button");
- copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
- copyButtonEl.onmousedown = (e) => {
- e.preventDefault();
- e.stopPropagation();
- };
-
- const linkIcon = "bi-clipboard";
- const checkIcon = "bi-check2";
-
- const shareIconEl = window.document.createElement("i");
- shareIconEl.setAttribute("class", `bi ${linkIcon}`);
- copyButtonEl.appendChild(shareIconEl);
- inputSuffixEl.prepend(copyButtonEl);
-
- const clipboard = new window.ClipboardJS(".aa-CopyButton", {
- text: function (_trigger) {
- const copyUrl = new URL(window.location);
- copyUrl.searchParams.set(kQueryArg, lastQuery);
- copyUrl.searchParams.set(kResultsArg, "1");
- return copyUrl.toString();
- },
- });
- clipboard.on("success", function (e) {
- // Focus the input
-
- // button target
- const button = e.trigger;
- const icon = button.querySelector("i.bi");
-
- // flash "checked"
- icon.classList.add(checkIcon);
- icon.classList.remove(linkIcon);
- setTimeout(function () {
- icon.classList.remove(checkIcon);
- icon.classList.add(linkIcon);
- }, 1000);
- });
- }
-
- // If there is a query, show the link icon
- if (copyButtonEl) {
- if (lastQuery && options["copy-button"]) {
- copyButtonEl.style.display = "flex";
- } else {
- copyButtonEl.style.display = "none";
- }
- }
- }
-}
-
-/* Search Index Handling */
-// create the index
-var fuseIndex = undefined;
-var shownWarning = false;
-
-// fuse index options
-const kFuseIndexOptions = {
- keys: [
- { name: "title", weight: 20 },
- { name: "section", weight: 20 },
- { name: "text", weight: 10 },
- ],
- ignoreLocation: true,
- threshold: 0.1,
-};
-
-async function readSearchData() {
- // Initialize the search index on demand
- if (fuseIndex === undefined) {
- if (window.location.protocol === "file:" && !shownWarning) {
- window.alert(
- "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
- );
- shownWarning = true;
- return;
- }
- const fuse = new window.Fuse([], kFuseIndexOptions);
-
- // fetch the main search.json
- const response = await fetch(offsetURL("search.json"));
- if (response.status == 200) {
- return response.json().then(function (searchDocs) {
- searchDocs.forEach(function (searchDoc) {
- fuse.add(searchDoc);
- });
- fuseIndex = fuse;
- return fuseIndex;
- });
- } else {
- return Promise.reject(
- new Error(
- "Unexpected status from search index request: " + response.status
- )
- );
- }
- }
-
- return fuseIndex;
-}
-
-function inputElement() {
- return window.document.body.querySelector(".aa-Form .aa-Input");
-}
-
-function focusSearchInput() {
- setTimeout(() => {
- const inputEl = inputElement();
- if (inputEl) {
- inputEl.focus();
- }
- }, 50);
-}
-
-/* Panels */
-const kItemTypeDoc = "document";
-const kItemTypeMore = "document-more";
-const kItemTypeItem = "document-item";
-const kItemTypeError = "error";
-
-function renderItem(
- item,
- createElement,
- state,
- setActiveItemId,
- setContext,
- refresh,
- quartoSearchOptions
-) {
- switch (item.type) {
- case kItemTypeDoc:
- return createDocumentCard(
- createElement,
- "file-richtext",
- item.title,
- item.section,
- item.text,
- item.href,
- item.crumbs,
- quartoSearchOptions
- );
- case kItemTypeMore:
- return createMoreCard(
- createElement,
- item,
- state,
- setActiveItemId,
- setContext,
- refresh
- );
- case kItemTypeItem:
- return createSectionCard(
- createElement,
- item.section,
- item.text,
- item.href
- );
- case kItemTypeError:
- return createErrorCard(createElement, item.title, item.text);
- default:
- return undefined;
- }
-}
-
-function createDocumentCard(
- createElement,
- icon,
- title,
- section,
- text,
- href,
- crumbs,
- quartoSearchOptions
-) {
- const iconEl = createElement("i", {
- class: `bi bi-${icon} search-result-icon`,
- });
- const titleEl = createElement("p", { class: "search-result-title" }, title);
- const titleContents = [iconEl, titleEl];
- const showParent = quartoSearchOptions["show-item-context"];
- if (crumbs && showParent) {
- let crumbsOut = undefined;
- const crumbClz = ["search-result-crumbs"];
- if (showParent === "root") {
- crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
- } else if (showParent === "parent") {
- crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
- } else {
- crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
- crumbClz.push("search-result-crumbs-wrap");
- }
-
- const crumbEl = createElement(
- "p",
- { class: crumbClz.join(" ") },
- crumbsOut
- );
- titleContents.push(crumbEl);
- }
-
- const titleContainerEl = createElement(
- "div",
- { class: "search-result-title-container" },
- titleContents
- );
-
- const textEls = [];
- if (section) {
- const sectionEl = createElement(
- "p",
- { class: "search-result-section" },
- section
- );
- textEls.push(sectionEl);
- }
- const descEl = createElement("p", {
- class: "search-result-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
- textEls.push(descEl);
-
- const textContainerEl = createElement(
- "div",
- { class: "search-result-text-container" },
- textEls
- );
-
- const containerEl = createElement(
- "div",
- {
- class: "search-result-container",
- },
- [titleContainerEl, textContainerEl]
- );
-
- const linkEl = createElement(
- "a",
- {
- href: offsetURL(href),
- class: "search-result-link",
- },
- containerEl
- );
-
- const classes = ["search-result-doc", "search-item"];
- if (!section) {
- classes.push("document-selectable");
- }
-
- return createElement(
- "div",
- {
- class: classes.join(" "),
- },
- linkEl
- );
-}
-
-function createMoreCard(
- createElement,
- item,
- state,
- setActiveItemId,
- setContext,
- refresh
-) {
- const moreCardEl = createElement(
- "div",
- {
- class: "search-result-more search-item",
- onClick: (e) => {
- // Handle expanding the sections by adding the expanded
- // section to the list of expanded sections
- toggleExpanded(item, state, setContext, setActiveItemId, refresh);
- e.stopPropagation();
- },
- },
- item.title
- );
-
- return moreCardEl;
-}
-
-function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
- const expanded = state.context.expanded || [];
- if (expanded.includes(item.target)) {
- setContext({
- expanded: expanded.filter((target) => target !== item.target),
- });
- } else {
- setContext({ expanded: [...expanded, item.target] });
- }
-
- refresh();
- setActiveItemId(item.__autocomplete_id);
-}
-
-function createSectionCard(createElement, section, text, href) {
- const sectionEl = createSection(createElement, section, text, href);
- return createElement(
- "div",
- {
- class: "search-result-doc-section search-item",
- },
- sectionEl
- );
-}
-
-function createSection(createElement, title, text, href) {
- const descEl = createElement("p", {
- class: "search-result-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
-
- const titleEl = createElement("p", { class: "search-result-section" }, title);
- const linkEl = createElement(
- "a",
- {
- href: offsetURL(href),
- class: "search-result-link",
- },
- [titleEl, descEl]
- );
- return linkEl;
-}
-
-function createErrorCard(createElement, title, text) {
- const descEl = createElement("p", {
- class: "search-error-text",
- dangerouslySetInnerHTML: {
- __html: text,
- },
- });
-
- const titleEl = createElement("p", {
- class: "search-error-title",
- dangerouslySetInnerHTML: {
- __html: ` ${title}`,
- },
- });
- const errorEl = createElement("div", { class: "search-error" }, [
- titleEl,
- descEl,
- ]);
- return errorEl;
-}
-
-function positionPanel(pos) {
- const panelEl = window.document.querySelector(
- "#quarto-search-results .aa-Panel"
- );
- const inputEl = window.document.querySelector(
- "#quarto-search .aa-Autocomplete"
- );
-
- if (panelEl && inputEl) {
- panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
- if (pos === "start") {
- panelEl.style.left = `${Math.round(inputEl.left)}px`;
- } else {
- panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
- }
- }
-}
-
-/* Highlighting */
-// highlighting functions
-function highlightMatch(query, text) {
- if (text) {
- const start = text.toLowerCase().indexOf(query.toLowerCase());
- if (start !== -1) {
- const startMark = "";
- const endMark = "";
-
- const end = start + query.length;
- text =
- text.slice(0, start) +
- startMark +
- text.slice(start, end) +
- endMark +
- text.slice(end);
- const startInfo = clipStart(text, start);
- const endInfo = clipEnd(
- text,
- startInfo.position + startMark.length + endMark.length
- );
- text =
- startInfo.prefix +
- text.slice(startInfo.position, endInfo.position) +
- endInfo.suffix;
-
- return text;
- } else {
- return text;
- }
- } else {
- return text;
- }
-}
-
-function clipStart(text, pos) {
- const clipStart = pos - 50;
- if (clipStart < 0) {
- // This will just return the start of the string
- return {
- position: 0,
- prefix: "",
- };
- } else {
- // We're clipping before the start of the string, walk backwards to the first space.
- const spacePos = findSpace(text, pos, -1);
- return {
- position: spacePos.position,
- prefix: "",
- };
- }
-}
-
-function clipEnd(text, pos) {
- const clipEnd = pos + 200;
- if (clipEnd > text.length) {
- return {
- position: text.length,
- suffix: "",
- };
- } else {
- const spacePos = findSpace(text, clipEnd, 1);
- return {
- position: spacePos.position,
- suffix: spacePos.clipped ? "โฆ" : "",
- };
- }
-}
-
-function findSpace(text, start, step) {
- let stepPos = start;
- while (stepPos > -1 && stepPos < text.length) {
- const char = text[stepPos];
- if (char === " " || char === "," || char === ":") {
- return {
- position: step === 1 ? stepPos : stepPos - step,
- clipped: stepPos > 1 && stepPos < text.length,
- };
- }
- stepPos = stepPos + step;
- }
-
- return {
- position: stepPos - step,
- clipped: false,
- };
-}
-
-// removes highlighting as implemented by the mark tag
-function clearHighlight(searchterm, el) {
- const childNodes = el.childNodes;
- for (let i = childNodes.length - 1; i >= 0; i--) {
- const node = childNodes[i];
- if (node.nodeType === Node.ELEMENT_NODE) {
- if (
- node.tagName === "MARK" &&
- node.innerText.toLowerCase() === searchterm.toLowerCase()
- ) {
- el.replaceChild(document.createTextNode(node.innerText), node);
- } else {
- clearHighlight(searchterm, node);
- }
- }
- }
-}
-
-function escapeRegExp(string) {
- return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-}
-
-// highlight matches
-function highlight(term, el) {
- const termRegex = new RegExp(term, "ig");
- const childNodes = el.childNodes;
-
- // walk back to front avoid mutating elements in front of us
- for (let i = childNodes.length - 1; i >= 0; i--) {
- const node = childNodes[i];
-
- if (node.nodeType === Node.TEXT_NODE) {
- // Search text nodes for text to highlight
- const text = node.nodeValue;
-
- let startIndex = 0;
- let matchIndex = text.search(termRegex);
- if (matchIndex > -1) {
- const markFragment = document.createDocumentFragment();
- while (matchIndex > -1) {
- const prefix = text.slice(startIndex, matchIndex);
- markFragment.appendChild(document.createTextNode(prefix));
-
- const mark = document.createElement("mark");
- mark.appendChild(
- document.createTextNode(
- text.slice(matchIndex, matchIndex + term.length)
- )
- );
- markFragment.appendChild(mark);
-
- startIndex = matchIndex + term.length;
- matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
- if (matchIndex > -1) {
- matchIndex = startIndex + matchIndex;
- }
- }
- if (startIndex < text.length) {
- markFragment.appendChild(
- document.createTextNode(text.slice(startIndex, text.length))
- );
- }
-
- el.replaceChild(markFragment, node);
- }
- } else if (node.nodeType === Node.ELEMENT_NODE) {
- // recurse through elements
- highlight(term, node);
- }
- }
-}
-
-/* Link Handling */
-// get the offset from this page for a given site root relative url
-function offsetURL(url) {
- var offset = getMeta("quarto:offset");
- return offset ? offset + url : url;
-}
-
-// read a meta tag value
-function getMeta(metaName) {
- var metas = window.document.getElementsByTagName("meta");
- for (let i = 0; i < metas.length; i++) {
- if (metas[i].getAttribute("name") === metaName) {
- return metas[i].getAttribute("content");
- }
- }
- return "";
-}
-
-function algoliaSearch(query, limit, algoliaOptions) {
- const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
-
- const applicationId = algoliaOptions["application-id"];
- const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
- const indexName = algoliaOptions["index-name"];
- const indexFields = algoliaOptions["index-fields"];
- const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
- const searchParams = algoliaOptions["params"];
- const searchAnalytics = !!algoliaOptions["analytics-events"];
-
- return getAlgoliaResults({
- searchClient,
- queries: [
- {
- indexName: indexName,
- query,
- params: {
- hitsPerPage: limit,
- clickAnalytics: searchAnalytics,
- ...searchParams,
- },
- },
- ],
- transformResponse: (response) => {
- if (!indexFields) {
- return response.hits.map((hit) => {
- return hit.map((item) => {
- return {
- ...item,
- text: highlightMatch(query, item.text),
- };
- });
- });
- } else {
- const remappedHits = response.hits.map((hit) => {
- return hit.map((item) => {
- const newItem = { ...item };
- ["href", "section", "title", "text", "crumbs"].forEach(
- (keyName) => {
- const mappedName = indexFields[keyName];
- if (
- mappedName &&
- item[mappedName] !== undefined &&
- mappedName !== keyName
- ) {
- newItem[keyName] = item[mappedName];
- delete newItem[mappedName];
- }
- }
- );
- newItem.text = highlightMatch(query, newItem.text);
- return newItem;
- });
- });
- return remappedHits;
- }
- },
- });
-}
-
-let subSearchTerm = undefined;
-let subSearchFuse = undefined;
-const kFuseMaxWait = 125;
-
-async function fuseSearch(query, fuse, fuseOptions) {
- let index = fuse;
- // Fuse.js using the Bitap algorithm for text matching which runs in
- // O(nm) time (no matter the structure of the text). In our case this
- // means that long search terms mixed with large index gets very slow
- //
- // This injects a subIndex that will be used once the terms get long enough
- // Usually making this subindex is cheap since there will typically be
- // a subset of results matching the existing query
- if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
- // Use the existing subSearchFuse
- index = subSearchFuse;
- } else if (subSearchFuse !== undefined) {
- // The term changed, discard the existing fuse
- subSearchFuse = undefined;
- subSearchTerm = undefined;
- }
-
- // Search using the active fuse
- const then = performance.now();
- const resultsRaw = await index.search(query, fuseOptions);
- const now = performance.now();
-
- const results = resultsRaw.map((result) => {
- const addParam = (url, name, value) => {
- const anchorParts = url.split("#");
- const baseUrl = anchorParts[0];
- const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
- anchorParts[0] = baseUrl + sep + name + "=" + value;
- return anchorParts.join("#");
- };
-
- return {
- title: result.item.title,
- section: result.item.section,
- href: addParam(result.item.href, kQueryArg, query),
- text: highlightMatch(query, result.item.text),
- crumbs: result.item.crumbs,
- };
- });
-
- // If we don't have a subfuse and the query is long enough, go ahead
- // and create a subfuse to use for subsequent queries
- if (
- now - then > kFuseMaxWait &&
- subSearchFuse === undefined &&
- resultsRaw.length < fuseOptions.limit
- ) {
- subSearchTerm = query;
- subSearchFuse = new window.Fuse([], kFuseIndexOptions);
- resultsRaw.forEach((rr) => {
- subSearchFuse.add(rr.item);
- });
- }
- return results;
-}
diff --git a/_proc/docs/sitemap.xml b/_proc/docs/sitemap.xml
deleted file mode 100644
index d117446..0000000
--- a/_proc/docs/sitemap.xml
+++ /dev/null
@@ -1,95 +0,0 @@
-
-*Convert CSV annotations to CVAT XML format.
Args: images_folder (str): Path to the folder containing the images. csvs_folder (str): Path to the folder containing the CSV annotations. xml_output_file (str): Path to the output XML file. items (list, optional): List of items to process. If None, all items will be processed. Defaults to None. parquet (bool, optional): Whether the annotations are stored in Parquet format. Defaults to False. relative_df (bool, optional): Whether the bounding box coordinates in the CSV are relative to the image size. Defaults to True. default_label (str, optional): Default label for the bounding boxes. Defaults to โBackgroundโ. extension (str, optional): Image file extension. Defaults to โjpgโ.
Returns: None*
++++yolo_2_df (yolo, h, w, id2class, class_column)
++df_2_yolo (df, h, w, class2id, class_column)
from torch_snippets import show, read, P, pd[08/23/24 18:12:57] WARNING Unable to load torch and dependent libraries from torch-snippets. d=185334;file:///Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/loader.py:108\loader.py;;\:d=169396;file:///Users/yeshwanth/Code/Personal/torch_snippets/torch_snippets/loader.py:108#<module>:108\<module>:108;;\
- Functionalities might be limited. pip install lovely-tensors in case there are torch related errors assets = P("/Users/yeshwanth.y/code/torch_snippets/assets/")
-im = read(assets / "Preamble.png")
-df = pd.read_csv(assets / "Preamble.csv")
-show(df.head())
-df = to_absolute(df, *im.shape[:2])
-show(df.head())
-df = to_relative(df, *im.shape[:2])
-show(df.head())assets = P("/Users/yeshwanth.y/code/torch_snippets/assets/")
+im = read(assets / "Preamble.png")
+df = pd.read_csv(assets / "Preamble.csv")
+show(df.head())
+df = to_absolute(df, *im.shape[:2])
+show(df.head())
+df = to_relative(df, *im.shape[:2])
+show(df.head())